U.S. patent application number 16/890889 was filed with the patent office on 2020-09-17 for method and system for providing artificial intelligence analytic (aia) services for performance prediction.
The applicant listed for this patent is Xevo Inc.. Invention is credited to John Palmer Cordell, John Hayes Ludwig, Samuel James McKelvie, Richard Chia Tsing Tong, Robert Victor Welland.
Application Number | 20200294403 16/890889 |
Document ID | / |
Family ID | 1000004867275 |
Filed Date | 2020-09-17 |
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United States Patent
Application |
20200294403 |
Kind Code |
A1 |
Tong; Richard Chia Tsing ;
et al. |
September 17, 2020 |
METHOD AND SYSTEM FOR PROVIDING ARTIFICIAL INTELLIGENCE ANALYTIC
(AIA) SERVICES FOR PERFORMANCE PREDICTION
Abstract
One embodiment of the present invention predicts a vehicular
event relating to machinal performance using information obtained
from interior and exterior sensors, vehicle onboard computer
("VOC"), and cloud data. The process of predication is able to
activate interior and exterior sensors mounted on a vehicle
operated by a driver for obtaining current data relating to
external surroundings, interior settings, and internal mechanical
conditions of the vehicle. After forwarding the current data to VOC
to generate a current vehicle status representing real-time vehicle
performance in accordance with the current data, retrieving a
historical data associated with the vehicle including mechanical
condition is retrieved. In one aspect, a normal condition signal is
issued when the current vehicle status does not satisfy with the
optimal condition based on the historical data. Alternatively, a
race car condition is issued when the current vehicle status meets
with the optimal condition.
Inventors: |
Tong; Richard Chia Tsing;
(Seattle, WA) ; Welland; Robert Victor; (Seattle,
WA) ; Ludwig; John Hayes; (Bellevue, WA) ;
Cordell; John Palmer; (Los Angeles, CA) ; McKelvie;
Samuel James; (Seattle, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Xevo Inc. |
Bellevue |
WA |
US |
|
|
Family ID: |
1000004867275 |
Appl. No.: |
16/890889 |
Filed: |
June 2, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
15852567 |
Dec 22, 2017 |
10713955 |
|
|
16890889 |
|
|
|
|
62438268 |
Dec 22, 2016 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G07C 5/10 20130101; G06Q
10/20 20130101; G06K 9/00791 20130101; G07C 5/006 20130101; H04N
7/181 20130101; H04L 12/40 20130101; G07C 5/008 20130101; G08G
1/143 20130101; H04L 2012/40215 20130101; G06N 5/04 20130101; G06N
20/00 20190101; G06K 9/00812 20130101; G06K 9/00845 20130101; B60R
16/0231 20130101; G07C 5/0808 20130101; G06N 5/022 20130101; G06Q
40/08 20130101; G06K 9/6288 20130101; B60Y 2400/3015 20130101; G07C
5/0841 20130101; G08G 1/168 20130101; G06N 7/005 20130101; G06Q
30/014 20130101 |
International
Class: |
G08G 1/16 20060101
G08G001/16; G06N 20/00 20060101 G06N020/00; G06K 9/00 20060101
G06K009/00; G06K 9/62 20060101 G06K009/62; G06Q 10/00 20060101
G06Q010/00; G07C 5/00 20060101 G07C005/00; B60R 16/023 20060101
B60R016/023; G06N 5/02 20060101 G06N005/02; G06N 7/00 20060101
G06N007/00; G06Q 30/00 20060101 G06Q030/00; G07C 5/08 20060101
G07C005/08; G07C 5/10 20060101 G07C005/10; H04L 12/40 20060101
H04L012/40; H04N 7/18 20060101 H04N007/18; G08G 1/14 20060101
G08G001/14 |
Claims
1. A method, comprising: activating a plurality of sensors
associated with a vehicle; obtaining data from the plurality of
sensors relating to external surroundings, interior settings, or
internal mechanical conditions of the vehicle; generating a current
vehicle status representing current vehicle performance based on
the obtained data; retrieving historical data associated with the
vehicle; and issuing a normal condition signal in response to the
current vehicle status failing to satisfy an optimal condition
based on the historical data.
2. The method of claim 1, further comprising: issuing a race-car
condition signal indicating the vehicle is used for racing in
response to the current vehicle status satisfying the optimal
condition based on the historical data.
3. The method of claim 1, further comprising: sending a performance
report to a third party indicating a current mechanical condition
of the vehicle based on the current vehicle performance and the
historical data.
4. The method of claim 1, wherein activating the plurality of
sensors includes: enabling one or more outward-facing cameras on
the vehicle to record an environment outside the vehicle.
5. The method of claim 1, wherein activating the plurality of
sensors includes: enabling one or more inward-facing cameras on the
vehicle to record an environment inside the vehicle.
6. The method of claim 1, wherein activating the plurality of
sensors includes: recording real-time data relating to at least one
of vehicle performance, road conditions, traffic congestion, or
weather conditions. 7 The method of claim 1, wherein issuing the
normal condition signal includes: determining that the current
vehicle status fails to satisfy the optimal condition based on the
current vehicle status failing meet performance requirements as
manufactured based on the historical data.
8. The method of claim 1, wherein issuing the normal condition
signal includes: determining that the current vehicle status fails
to satisfy the optimal condition based on the current vehicle
status indicating signs of wear and tear.
9. The method of claim 1, further comprising: scheduling a
maintenance appointment for the vehicle based on the current
vehicle status.
10. The method of claim 1, further comprising: initiating a
manufacture recall related to the vehicle at least partially based
on the current vehicle status.
11. A computing system, comprising: a memory that stores computer
instructions; and a processor that executes the computer
instruction to: activate a plurality of sensors associated with a
vehicle; obtain data from the plurality of sensors relating to
external surroundings, interior settings, or internal mechanical
conditions of the vehicle; generate a current vehicle status
representing current vehicle performance based on the obtained
data; retrieve historical data associated with the vehicle; and
issue a race-car condition signal indicating the vehicle is used
for racing in response to the current vehicle status satisfying an
optimal condition based on the historical data.
12. The computing system of claim 11, wherein the processor
executes further computer instructions to: issue a normal condition
signal in response to the current vehicle status failing to satisfy
an optimal condition based on the historical data.
13. The computing system of claim 11, wherein the processor
executes further computer instructions to: send a performance
report to a third party indicating a current mechanical condition
of the vehicle based on the current vehicle performance and the
historical data.
14. The computing system of claim 11, wherein the processor
executes further computer instructions to: send a performance
report to a third party indicating skills or mistakes associated
with a driver of the vehicle based on the current vehicle
performance and the historical data.
15. The computing system of claim 11, wherein the processor issues
the race-car condition signal by further executing further computer
instructions to: determine that the current vehicle status
satisfies the optimal condition based on the current vehicle status
meeting performance requirements as manufactured and failing to
indicate signs of wear and tear.
16. A non-transitory computer-readable storage medium having stored
thereon instructions that, when executed by a processor, cause the
processor to perform actions, the actions comprising: activating a
plurality of sensors associated with a vehicle; obtaining data from
the plurality of sensors relating to external surroundings,
interior settings, or internal mechanical conditions of the
vehicle; generating a current vehicle status representing current
vehicle performance based on the obtained data; retrieving
historical data associated with the vehicle; and sending a
performance report to a third party indicating a current mechanical
condition of the vehicle based on the current vehicle performance
and the historical data.
17. The non-transitory computer-readable storage medium of claim
16, further comprising: issuing a race-car condition signal
indicating the vehicle is used for racing in response to the
current vehicle status satisfying the optimal condition based on
the historical data.
18. The non-transitory computer-readable storage medium of claim
16, further comprising: issuing a normal condition signal in
response to the current vehicle status failing to satisfy an
optimal condition based on the historical data.
19. The non-transitory computer-readable storage medium of claim
16, further comprising: scheduling a maintenance appointment for
the vehicle based on the current vehicle status.
20. The non-transitory computer-readable storage medium of claim
16, further comprising: initiating a manufacture recall related to
the vehicle at least partially based on the current vehicle status.
Description
PRIORITY
[0001] This application claims the benefit of priority based upon
U.S. Provisional Patent Application having an application Ser. No.
62/438,268, filed on Dec. 22, 2016, and having a title of "Method
and System for Providing Artificial Intelligence (AI) Analytic
Services Using Cloud and Embedded Data," and U.S. non-provisional
Patent Application having an application Ser. No. 15/852,567, filed
on Dec. 22, 2017 and having a title of "Method and System for
Providing Artificial Intelligence Analytic (AIA) Services for
Performance Prediction," which are hereby incorporated by reference
in its entirety.
FIELD
[0002] The exemplary embodiment(s) of the present invention relates
to the field of communication networks. More specifically, the
exemplary embodiment(s) of the present invention relates to
providing automation relating to vehicles using artificial
intelligence ("AI") modules and cloud computing.
BACKGROUND
[0003] With rapid integration of motor vehicle with wireless
network, AI, and IoT (Internet of Things), the demand of
intelligent machine and instant response is constantly growing. For
example, the cars or vehicles which become smarter can assist
drivers to operate the vehicles. To implement the integration of
vehicle and AI, some technical pieces, such as data management,
model training, and data collection, need to be improved. The
conventional machine learning process, for example, is generally an
exploratory process which may involve trying different kinds of
models, such as convolutional, RNN (recurrent neural network),
attentional, et cetera.
[0004] Machine learning or model training typically concerns a wide
variety of hyper-parameters that change the shape of the model and
training characteristics. Model training generally requires
intensive computation and data collection. With conventional data
collection via IoT, AI, real-time images, videos, and/or machine
learning, the size of data (real-time data, cloud data, big data,
etc.) is voluminous and becomes difficult to handle and digest. As
such, real-time response via machine learning model with massive
data processing can be challenging. Another drawback associated
with large data processing and machine learning for model
improvements is that it is often difficult to translate collected
data into useful information.
SUMMARY
[0005] One embodiment of the present invention discloses an
artificial intelligence analytic ("AIA") process of providing a
prediction or report capable of predicting an event relating to
vehicle performance using data obtained from interior and exterior
sensors, vehicle onboard computer ("VOC"), and cloud computing. The
process activates the interior and exterior sensors mounted on a
vehicle operated by a driver for obtaining current data relating to
external surroundings, interior settings, and internal mechanical
conditions of the vehicle. For example, after enabling a set of
outward facing cameras mounted on the vehicle for recording
external surrounding images representing a geographic environment,
one or more inward facing cameras mounted in the vehicle is
initiated for collecting interior images of the vehicle. Also, a
set of internal sensors attached to various mechanical components
is activated for measuring temperatures, functionalities, or audio
sounds associated with mechanical components within the vehicle.
The process, in one embodiment, is capable of detecting driver's
response time based on a set of identified road conditions and
information from a controller area network ("CAN") bus of the
vehicle. The real-time data relating to vehicle performance, road
condition, traffic congestion, and weather condition is recorded.
The current data is forwarded to VOC for generating a current
vehicle status representing substantially real-time vehicle
performance in accordance with the current data. The historical
data associated with the vehicle including mechanical condition is
retrieved. Note that the historical data is updated in response to
the current data.
[0006] A normal condition signal is issued when the current vehicle
status does not satisfy with optimal condition based on the
historical data. In one aspect, after uploading the current vehicle
status to a vehicle performance predictor which resides at least
partially at a cloud via a communications network, the big data is
obtained from the cloud wherein the big data represents large car
samples having similar attributes as the vehicle. For example, the
big data accumulates information from cars with similar brands,
similar mileages, similar years, similar geographic location, and
similar drivers. The current vehicle status is compared with the
big data and the historical data to assess whether the vehicle
operates in a normal condition. The "race car ready" condition is
issued when the current vehicle status meets with the optimal
condition based on the historical data. In one example, the current
vehicle status is forwarded to a subscriber for evaluating driver's
driving skill. The current vehicle status can also be forwarded to
a subscriber for assessing normal wearing and tearing.
Alternatively, a subscriber schedules a maintenance or repair
appointment with the driver based on the current vehicle status. In
one embodiment, a manufacture can initiate a recall for automobiles
similar to the vehicle at least partially based on the current
vehicle status.
[0007] An AIA system capable of providing a vehicle performance
prediction relating to an automobile operated by a driver includes
sensors, VOC, and cloud network. In one aspect, a group of interior
and exterior sensors mounted on a vehicle and internal mechanical
components are configured to collect information relating to
external surroundings, interior environment, and components
conditions. For example, the interior and exterior sensors include
outward facing cameras mounted on a vehicle collecting external
images representing a surrounding environment in which the vehicle
operates and inward facing cameras mounted inside of the vehicle
collecting interior images including operator facial expression and
operator's attention.
[0008] VOC is used to generate a current vehicle status which
represents a current vehicle condition in accordance with the
obtained information. The cloud network, in one embodiment,
includes a normal condition module, prediction module, and race car
module. The normal condition module determines a normal condition
for the vehicle based on the current vehicle status, historical
vehicle status, and big data, wherein the big data represents a
large sample having similar attributes as the vehicle. The
prediction module is capable of predicting vehicle failure in
response to the normal condition. The race car module is able to
provide skills and/or mistakes associated with a race car driver
operating the vehicle based on the current vehicle status,
historical vehicle status, and big data which is a large set of
accumulated samples having similar characteristics as the vehicle.
It should be noted that the cloud network also includes a
subscription module for facilitating and enlisting subscribers.
[0009] In an alternative embodiment, an AIA system or process able
to accumulate information relating to machinal performance and
drivers in accordance with information obtained from interior and
exterior sensors, VOC, and cloud network is capable of activating
interior and exterior sensors mounted inside and outside of a
vehicle operated by a driver for collecting real-time information
relating to external surroundings, interior settings, and internal
conditions of the vehicle. After forwarding the real-time
information to the VOC to generate a current vehicle status
representing substantial real-time vehicle performance in
accordance with the real-time information, the current vehicle
status is uploaded to the cloud network and retrieving a historical
data associated with the vehicle including performance data. Upon
generating a vehicle performance report in response to the
historical data and big data containing relatively large samples
having similar attributes as the vehicle, a maintenance appointment
is scheduled by a repair shop with the driver based on the vehicle
performance report. The vehicle performance report can also be
forwarded to a subscribed automobile manufacturer for vehicle
recalls. Moreover, the vehicle performance report can also be sent
to a subscriber owner of the vehicle indicating current mechanical
condition of the vehicle.
[0010] Additional features and benefits of the exemplary
embodiment(s) of the present invention will become apparent from
the detailed description, figures and claims set forth below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The exemplary embodiment(s) of the present invention will be
understood more fully from the detailed description given below and
from the accompanying drawings of various embodiments of the
invention, which, however, should not be taken to limit the
invention to the specific embodiments, but are for explanation and
understanding only.
[0012] FIGS. 1A-1B are block diagrams illustrating artificial
intelligence analytic service ("AIAS") capable of predicting
vehicle mechanical performance ("VMP") using a virtuous cycle in
accordance with one embodiment of the present invention;
[0013] FIG. 1C is a block diagram illustrating a process of
generating reports relating to vehicle as well as driver status
using an AIA model via a virtuous cycle in accordance with one
embodiment of the present invention;
[0014] FIG. 1D is a block diagram illustrating an integrated
development environment ("IDE") configured to host various models
for providing AIAS via a virtuous cycle in accordance with one
embodiment of the present invention;
[0015] FIG. 1E is a logic diagram illustrating an AIA system
configured to provide a prediction relating to vehicle mechanical
performance ("VMP") via a virtuous cycle in accordance with one
embodiment of the present invention;
[0016] FIG. 1F is a logic diagram illustrating an AIA system
facilitated configured to provide a vehicle failure prediction
("VFP") via a virtuous cycle in accordance with one embodiment of
the present invention;
[0017] FIG. 1G is a logic diagram illustrating an AIA process
facilitated by AIA system configured to provide VMP using big data
via a virtuous cycle in accordance with one embodiment of the
present invention;
[0018] FIGS. 2A-2B are block diagrams illustrating a virtuous cycle
capable of facilitating AIAS using IA model in accordance with one
embodiment of the present invention;
[0019] FIG. 3 is a block diagram illustrating a cloud based network
using crowdsourcing approach to improve IA model(s) for AIAS in
accordance with one embodiment of the present invention;
[0020] FIG. 4 is a block diagram illustrating an IA model or AIA
system using the virtuous cycle in accordance with one embodiment
of the present invention;
[0021] FIG. 5 is a block diagram illustrating an exemplary process
of correlating data for AIAS in accordance with one embodiment of
the present invention;
[0022] FIG. 6 is a block diagram illustrating an exemplary process
of real-time data management for AI model for AIAS in accordance
with one embodiment of the present invention;
[0023] FIG. 7 is a block diagram illustrating a crowd sourced
application model for AI model for AIAS in accordance with one
embodiment of the present invention;
[0024] FIG. 8 is a block diagram illustrating a method of storing
AI related data using a geo-spatial objective storage for AIAS in
accordance with one embodiment of the present invention;
[0025] FIG. 9 is a block diagram illustrating an exemplary approach
of analysis engine analyzing collected data for AIAS in accordance
with one embodiment of the present invention;
[0026] FIG. 10 is a block diagram illustrating an exemplary
containerized sensor network used for sensing information for AIAS
in accordance with one embodiment of the present invention;
[0027] FIG. 11 is a block diagram illustrating a processing device
VOC, and/or computer system which can be installed in a vehicle for
facilitating the virtuous cycle in accordance with one embodiment
of the present invention; and
[0028] FIG. 12 is a flowchart illustrating a process of AIA system
for AIAS capable of providing VMP in accordance with one embodiment
of the present invention.
DETAILED DESCRIPTION
[0029] Embodiments of the present invention are described herein
with context of a method and/or apparatus for providing prediction
services using cloud data, embedded data, and machine learning
center ("MLC").
[0030] The purpose of the following detailed description is to
provide an understanding of one or more embodiments of the present
invention. Those of ordinary skills in the art will realize that
the following detailed description is illustrative only and is not
intended to be in any way limiting. Other embodiments will readily
suggest themselves to such skilled persons having the benefit of
this disclosure and/or description.
[0031] In the interest of clarity, not all of the routine features
of the implementations described herein are shown and described. It
will, of course, be understood that in the development of any such
actual implementation, numerous implementation-specific decisions
may be made in order to achieve the developer's specific goals,
such as compliance with application- and business-related
constraints, and that these specific goals will vary from one
implementation to another and from one developer to another.
Moreover, it will be understood that such a development effort
might be complex and time-consuming, but would nevertheless be a
routine undertaking of engineering for those of ordinary skills in
the art having the benefit of embodiment(s) of this disclosure.
[0032] Various embodiments of the present invention illustrated in
the drawings may not be drawn to scale. Rather, the dimensions of
the various features may be expanded or reduced for clarity. In
addition, some of the drawings may be simplified for clarity. Thus,
the drawings may not depict all of the components of a given
apparatus (e.g., device) or method. The same reference indicators
will be used throughout the drawings and the following detailed
description to refer to the same or like parts.
[0033] In accordance with the embodiment(s) of present invention,
the components, process steps, and/or data structures described
herein may be implemented using various types of operating systems,
computing platforms, computer programs, and/or general-purpose
machine. In addition, those of ordinary skills in the art will
recognize that devices of less general-purpose nature, such as
hardware devices, field programmable gate arrays (FPGAs),
application specific integrated circuits (ASICs), or the like, may
also be used without departing from the scope and spirit of the
inventive concepts disclosed herein. Where a method comprising a
series of process steps is implemented by a computer or a machine
and those process steps can be stored as a series of instructions
readable by the machine, they may be stored on a tangible medium
such as a computer memory device (e.g., ROM (Read Only Memory),
PROM (Programmable Read Only Memory), EEPROM (Electrically Erasable
Programmable Read Only Memory), FLASH Memory, Jump Drive, and the
like), magnetic storage medium (e.g., tape, magnetic disk drive,
and the like), optical storage medium (e.g., CD-ROM, DVD-ROM, paper
card and paper tape, and the like) and other known types of program
memory.
[0034] The term "system" or "device" is used generically herein to
describe any number of components, elements, sub-systems, devices,
packet switch elements, packet switches, access switches, routers,
networks, computer and/or communication devices or mechanisms, or
combinations of components thereof. The term "computer" includes a
processor, memory, and buses capable of executing instruction
wherein the computer refers to one or a cluster of computers,
personal computers, workstations, mainframes, or combinations of
computers thereof.
[0035] One embodiment of the present invention predicts a vehicular
event relating to machinal performance using information obtained
from interior and exterior sensors, vehicle onboard computer
("VOC"), and cloud data. The process of predication is able to
activate interior and exterior sensors mounted on a vehicle
operated by a driver for obtaining current data relating to
external surroundings, interior settings, and internal mechanical
conditions of the vehicle. After forwarding the current data to VOC
to generate a current vehicle status representing real-time vehicle
performance in accordance with the current data, retrieving a
historical data associated with the vehicle including mechanical
condition is retrieved. In one aspect, a normal condition signal is
issued when the current vehicle status does not satisfy with the
optimal condition based on the historical data. Alternatively, a
race car condition is issued when the current vehicle status meets
with the optimal condition.
[0036] FIG. 1A is a block diagram 100 illustrating artificial
intelligence analytic service ("AIAS") capable of predicting
vehicle mechanical performance ("VMP") using a virtuous cycle in
accordance with one embodiment of the present invention. Diagram
100 illustrates a virtuous cycle containing a vehicle 102, cloud
based network ("CBN") 104, and machine learning center ("MLC") 106.
In one aspect, MCL 106 can be located remotely or in the cloud.
Alternatively, MCL 106 can be a part of CBN 104. It should be noted
that the underlying concept of the exemplary embodiment(s) of the
present invention would not change if one or more blocks (circuit
or elements) were added to or removed from diagram 100.
[0037] Vehicle 102, in one example, can be a car, automobile, bus,
train, drone, airplane, truck, and the like, and is capable of
moving geographically from point A to point B. To simplify forgoing
discussing, the term "vehicle" or "car" is used to refer to car,
automobile, bus, train, drone, airplane, truck, motorcycle, and the
like. Vehicle 102 includes wheels with ABS (anti-lock braking
system), auto body, steering wheel 108, exterior or outward facing
cameras 125, interior (or 360.degree. (degree)) or inward facing
camera(s) 126, antenna 124, onboard controller or VOC 123, and
operator (or driver) 109. It should be noted that outward facing
cameras and/or inward facing cameras 125-126 can be installed at
front, side, top, back, and/or inside of vehicle 102. In one
example, vehicle 102 also includes various sensors which senses
mechanical related data associated with the vehicle, vehicle
status, and/or driver actions. For example, the sensors, not shown
in FIG. IA, can also collect other relevant information, such as
audio, ABS, steering, braking, acceleration, traction control,
windshield wipers, GPS (global positioning system), radar, sonar,
ultrasound, lidar (Light Detection and Ranging), and the like.
[0038] VOC or onboard controller 123 includes CPU (central
processing unit), GPU (graphic processing unit), memory, and disk
responsible for gathering data from outward facing or exterior
cameras 125, inward facing or interior cameras 126, audio sensor,
ABS, traction control, steering wheel, CAN-bus sensors, and the
like. In one aspect, VOC 123 executes IA model received from MLC
106, and uses antenna 124 to communicate with CBN 104 via a
wireless communication network 110. Note that wireless
communication network includes, but not limited to, WIFI, cellular
network, Bluetooth network, satellite network, or the like. A
function of VOC 123 is to gather or capture real-time surrounding
information as well as exterior information when vehicle 102 is
moving.
[0039] CBN 104 includes various digital computing systems, such as,
but not limited to, server farm 120, routers/switches 121, cloud
administrators 119, connected computing devices 116-117, and
network elements 118. A function of CBN 104 is to provide cloud
computing which can be viewed as on-demand Internet based computing
service with enormous computing power and resources. Another
function of CBN 104 is to improve or inferred attentional labeled
data via correlating captured real-time data with relevant cloud
data.
[0040] MLC 106, in one embodiment, provides, refines, trains,
and/or distributes models 115 such as AI model based on information
or data which may be processed and sent from CBN 104. It should be
noted that the machine learning makes predictions based on models
generated and maintained by various computational algorithms using
historical data as well as current data. A function of MLC 106 is
that it is capable of pushing information such as revised AI model
or prediction model to vehicle 102 via a wireless communications
network 114 constantly or in real-time.
[0041] To identify or collect operator attention (or ability) of
vehicle 102, an onboard AI model which could reside inside of VOC
123 receives a triggering event or events from built-in sensors
such as ABS, wheel slippery, turning status, engine status, and the
like. The triggering event or events may include, but not limited
to, activation of ABS, rapid steering, rapid breaking, excessive
wheel slip, activation of emergency stop, and on. Upon receiving
triggering events via vehicular status signals, the recording or
recorded images captured by inward facing camera or 360 camera are
forwarded to AIA system which resides at CBN 104.
[0042] In one embodiment, triggering events indicate an
inattentional, distracted, and/or dangerous driver. For example,
upon detecting a potential dangerous event, CBN 104 issues warning
signal to driver or operator 109 via, for instance, a haptic
signal, or shock to operator 109 notifying a potential collision.
In addition, the dangerous event or events are recorded for report.
It should be noted that a report describing driver's behavior as
well as number occurrence relating to dangerous events can be
useful. For example, such report can be obtained by insurance
company for insurance auditing, by law enforcement for accident
prevention, by city engineers for traffic logistics, or by medical
stuff for patient safety.
[0043] During an operation, inward facing camera 126 captures
facial images of driver or operator 109 including the location in
which operator's eyes focusing. Upon verifying with CBN 104, a
focal direction 107 of operator 109 is identified. After obtaining
and processing external images relating to focal direction 107, a
possible trajectory 105 in which the location is looked at is
obtained. Trajectory 105 and focal direction 107 are subsequently
processed and combined in accordance with stored data in the cloud.
The object, which is being looked at by operator 109, is
identified. In this example, the object is a house 103 nearby the
road.
[0044] The AIA system records and examines various status such as
pedal position, steering wheel position, mirror setting, seat
setting, engine RPM, whether the seat belts are clipped in,
internal and external temperature, et cetera. With the advent of
machine learning, a broad class of derived data and metadata can be
extracted from sensors and be used to improve the user experience
of being in or driving a vehicle. It should be noted that the
extracted data includes confidence and probability metrics for each
data element that the machine learning models generate. Such data,
which changes in real-time, is presented to an application layer
that can use the full context of vehicle in real-time.
[0045] Operator 109, in one aspect, can be any driver capable of
operating a vehicle. For example, operator 109 can be a teen
driver, elderly driver, professional race driver, fleet driver(s),
and the like. The fleet drivers can be, but not limited to, UPS
(United Parcel Service) drivers, police officers, Federal Express
drivers, taxi drivers, Uber drivers, Lyft drivers, delivery
drivers, bus drivers, and the like.
[0046] An advantage of using an AIAS is to leverage cloud
information as well as embedded data to generate a report of VMP
which can reduce traffic accidents and enhance public safety by
improving vehicle mechanical condition.
[0047] FIG. 1B is a block diagram 140 illustrating artificial
intelligence analytic service ("AIAS") capable of predicting
vehicle mechanical performance ("VMP") using a virtuous cycle in
accordance with one embodiment of the present invention. Diagram
140 illustrates a driver 148, inward facing camera(s) 142, and
exterior camera 144. In one aspect, camera 142, also known as
interior camera or 360 degree camera, monitors or captures driver's
facial expression 146 and/or driver (or operator) body language.
Upon reading status 149 which indicates stable with accelerometer,
ahead with gaze, hands on steering wheel (no texting), the AIA
model concludes that driver is behaving normally. In one example,
driver's identity ("ID") can be verified using images captured by
interior camera 142.
[0048] AIA model, for example, is able to detect which direction
driver 148 is looking, whether driver 148 is distracted, whether
driver 148 is texting, whether identity of driver is determined via
a facial recognition process, and/or where driver 148 pays
attention. It should be noted that the car may contain multiple
forward-facing cameras (or 360-degree camera(s)) 144 capable of
capturing a 360 degree view which can be used to correlate with
other views to identify whether driver 148 checks back-view mirror
to see cars behind the vehicle or checks at side view of vehicle
when the car turns. Based on observed information, the labeled data
showing looking at the correct spots based on traveling route of
car can illustrate where the driver pays attention. Alternatively,
the collected images or labeled data can be used to retrain the AIA
model which may predict the safety rating for driver 148.
[0049] During an operation, the interior images captured by inward
facing camera(s) 142 can show a location in which operator 148 is
focusing based on relative eye positions of operator 148. Once the
direction of location such as direction 145 is identified, the AIA
model obtains external images captured by outward facing camera(s)
144. After identifying image 145 is where operator pays attention
based on direction 145, the image 147 is recorded and process.
Alternatively, if AIA model expects operator 148 to look at the
direction 145 based on current speed and traffic condition while
detecting operator 148 actually looking at a house 141 based in
trajectory view 143 based on the captured images, a warning signal
will be activated.
[0050] It should be noted that the labeled data should include
various safety parameters such as whether the driver looks left
and/or right before crossing an intersection and/or whether the
driver gazes at correct locations while driving. The AIA model
collects data from various sensors, such as Lidar, radar, sonar,
thermometers, audio detector, pressure sensor, airflow, optical
sensor, infrared reader, speed sensor, altitude sensor, and the
like, to establish operating environment. The information can
change based on occupant(s) behavior in the vehicle or car. For
example, if occupants are noisy, loud radio, shouting, drinking,
eating, dancing, such behavior(s) can affect overall parameters as
bad driving behavior.
[0051] FIG. 1C is a block diagram 130 illustrating a process of
generating reports relating to driver status using an AIA model via
a virtuous cycle in accordance with one embodiment of the present
invention. Diagram 130 includes vehicle 131, cloud 132, and
subscriber 133 wherein cloud 132 can further includes machine
learning centers, historical driver data, and big data. In one
embodiment, the AIA model, at least partially residing in cloud
132, is capable of providing AIAS to subscriber(s) 133. It should
be noted that the underlying concept of the exemplary embodiment(s)
of the present invention would not change if one or more blocks
(circuit or elements) were added to or removed from diagram
130.
[0052] Vehicle 131 includes an infotainment unit 134, smart phone
135, VOC 136, and antenna 139. In one embodiment, infotainment unit
134 is coupled to head-end unit such as VOC 136 to collect
information about driving habit, skill, and/or ability associated
with an operator based on driver's condition, exterior environment,
and internal equipment/vehicle status. Driver's condition includes
driver ID, detected distractions such as talking over a phone,
texting, occupant distraction, and the like. Exterior environment
refers to traffic condition, road condition, whether condition,
and/or nearby drivers. The equipment or vehicle status indicates
automobile mechanical conditions, such as ABS application, sharp
steering, hard braking, sudden acceleration, traction control
activation, windshield wipers movement, and/or airbag deployment.
The collected information is subsequently forwarded to cloud 132
for processing.
[0053] Subscriber 133, in one example, can be insurance company,
family members, law enforcement, car dealers, auto manufactures,
and/or fleet companies such as Uber.TM. or UPS.TM.. In one aspect,
subscriber 133 is an insurance company which wants to assess risks
associated with certain group of drivers such as teen drivers or
elderly drivers based on prediction reports generated by AIAS. For
example, upon receipt of collected information from vehicle 131 to
cloud 132 as indicated by numeral 137, AIAS in cloud 132 generates
a prediction report associated with a particular driver based on
the driver's historical data as well as big data. The prediction
report is subsequently forwarded to subscriber 133 from cloud 132
as indicated by number 138.
[0054] Smart phone 135, which can be an iPhone.TM. or Android.TM.
phone, can be used for identifying driver's ID as well as provides
communication to cloud 132 via its cellular network access. Smart
phone 135 can also be used to couple to VOC 136 for facilitating
hyperscale or data scale from cloud data to embedded data.
Similarly, the embedded data can also be scaled before passing onto
the cloud.
[0055] An advantage of employing AIAS is that it can provide a
prediction relating to VMP and/or VFP in connection to a group of
vehicles to provide vehicle intelligence to drivers as well as
subscribers. For example, AIAS can predict or warn a possible
mechanical failure or flat tire in a near future.
[0056] FIG. 1D is a block diagram 150 illustrating an IDE 152
configured to host various models for providing AIAS via a virtuous
cycle in accordance with one embodiment of the present invention.
Diagram 150 includes a core cloud system 151, IDE 152, driver data
module 153, vehicle data module 154, geodata module 155, and
applications marketplace 156. While IDE 152 resides within core
cloud system 151, IDE 152 are configured to host various modules
such as modules 153-156. It should be noted that the underlying
concept of the exemplary embodiment(s) of the present invention
would not change if one or more blocks (modules or elements) were
added to or removed from diagram 150.
[0057] Driver data module 153, in one embodiment, includes a teen
driver detector, elder driver detector, and distracted driver
detector. The teen driver detector, in one example, monitors teen
drivers based on a set of predefined rules. The predefined rules
are often set by a subscriber such as an insurance company or
parents. The elder driver detector is responsible to monitor
elderly drivers' ability to continue driving according to a set of
predefined rules. Based on the detected and/or collected data, a
prediction report can be automatically generated and forwarded to
subscriber(s) in an agreed or subscribed time interval. The
distracted driver detector, in one embodiment, is used to detect
distracted or disabled drivers and reports such distracted drivers
to authority for enhancing public safety. Upon collecting data from
teen driver detector, elder driver detector, and distracted driver
detector, driver data module 153 forwards the collected data to IDE
152 for AIAS processing.
[0058] Vehicle data module 154 includes a performance analyzer,
predictive failure analyzer, and fleet manager. The performance
analyzer, in one example, is used to analyze and verify internal
vehicle mechanical performance. For instance, tire slippage may be
detected by the performance analyzer. The predictive failure
analyzer monitors vehicle maintenance and/or repair before the
failure of a particular part or device. The fleet manager, in one
example, is used to monitor its fleet cars. For example, UPS tracks
and/or Uber vehicles can be tracked and analyzed to predict the
operating efficiency and potential accidents. For example, after
receipt of data from performance analyzer, predictive failure
analyzer, and fleet manager, vehicle data module 154 forwards the
collected data to IDE 152 for AIAS processing.
[0059] Geodata module 155 includes a traffic router, hazard
detector, and parking detector. The traffic router, in one aspect,
is used to provide a real-time alternative route in the present
traffic congestion. In one embodiment, the traffic router is able
to communicate with other nearby vehicles, stationary street
cameras, and/or nearby drones to obtain current situation. For
instance, the traffic router can obtain reason(s) for congestion
and based on the reason, such as an accident, road construction,
sinkhole, damaged bridge, or slow walker, an alternative route(s)
may be provided. The Hazard detector, in one embodiment, detects
hazard conditions such as potholes, chemical spills, and/or road
obstacles. The parking detector, in one embodiment, is able to
automatically identify where the vehicle can park, how long the
vehicle had parked, how much parking fee should be assessed. After
receipt of data from traffic router, hazard detector, and parking
detector, geodata module 155 forwards the collected data to IDE 152
for AIAS processing.
[0060] Applications marketplace 156 includes maintenance scheduler,
micro insurance, and third-party modules. Applications marketplace
156, in one aspect, facilitates third-party communications,
software updates, applications, third-party modules, and the like.
Third-party includes insurance company, car deals, car repair
shops, police, government agencies, city transportation, and/or
other subscribers. In one aspect, Applications marketplace 156 is
configured receive subscriptions as well as sending prediction
reports to subscribers based on a set of predefined time
intervals.
[0061] In one embodiment, an AIA system capable of predicting an
event or risk associated with an operator driving a vehicle
includes multiple interior and exterior sensors, VOC, core cloud
system or cloud. Cloud 151, in one example, includes an IDE 152
configured to host driver data module 153, vehicle data module 154,
geodata module 155, and application marketplace module 156. In one
aspect, the AIA system is able to predict a future event or
potential risk based on current data, driver's historical data, and
big data. The vehicle data indicates a collection of attributes,
such as driving speed, braking frequency, sudden acceleration, ABS
triggering, geographic locations, driver's personal records, and/or
detected distractions, to describe driver's behavior, skill,
cognitive condition, ability, and/or physical condition. The big
data, in one example, refers to a set of data collected from large
population having similar attributes as the targeted driver's
attributes. For example, a targeted driver is a teen age driver,
the large population would be teen age drivers.
[0062] The interior and exterior sensors, in one example, installed
on a vehicle collect real-time data relating to external
surroundings and interior settings. The vehicle or car is operated
by the driver or targeted driver. The exterior sensors include
outward facing cameras for collecting external images representing
a surrounding environment in which the vehicle operates. The
interior sensors include inward facing cameras for collecting
interior images inside of vehicle including operator facial
expression as well as operator's attention. The external images
include real-time images relating to road, buildings, traffic
lights, pedestrian, or retailers. The interior images include a set
of interior sensors obtaining data relating to at least one of
operator's eyes, facial expression, driver, and passage. It should
be noted that interior and exterior cameras can detect a direction
in which the operator is looking.
[0063] The VOC, in one example, is configured to generate a current
data representing current real-time status in accordance with the
collected data. For instance, the VOC is able to identify
operator's driving ability in response to the collected internal
images and the collected external images. In addition, driver or
operator's ID can also be verified by the VOC.
[0064] Driver data module 153, in one aspect, includes a teen
driver detector, elder driver detector, and distracted driver
detector and is able to assess future predictions. The teen driver
detector is able to report teen's driving behavior to a subscriber
based on the current data and historical data. For example,
depending on the subscription supplied by the subscriber, the
report relating to teen's driving behavior or ability can be
periodically generated and sent to subscribers. The elder driver
detector is also able to report elder's driving behavior to a
subscriber based on the current data and historical data.
[0065] Vehicle data module 154 contains a performance analyzer,
predictive failure analyzer, fleet manager for collecting vehicle
related data. Geodata module 155 includes a traffic router, hazard
detector, and parking detector for detecting locations or physical
locations. Application marketplace module 156 contains a
maintenance scheduler and micro insurance for facilitating and/or
enlisting subscribers. For example, the micro insurance is able to
generate a report describing teen's driving behavior to an
insurance subscriber according to the big data, current data, and
historical data.
[0066] The AIA system, in one aspect, further includes audio
sensors configured to provide metadata relating to audio sound
which occurs outside the vehicle or inside the vehicle. For
example, the AIA system may include exterior audio sensors
collecting exterior sound outside of the vehicle. Similarly,
multiple interior audio sensors may be used to collect sound inside
of the vehicle. It should be noted that application marketplace
module 156 includes a third-party module which is able to host
various third-party applications, such as, but not limited to,
interactive advertisement, driverless vehicle application, drone
application, and the like.
[0067] An advantage of using the AIA system is that it is able to
facilitate AIAS to provide VMP using detected real-time data,
historical data, and big data.
[0068] FIG. 1E is a logic diagram 160 illustrating an AIA system
configured to provide a prediction relating to vehicle mechanical
performance ("VMP") via a virtuous cycle in accordance with one
embodiment of the present invention. Diagram 160 includes a
real-time information module 161, historical data 170, cloud big
data 171, optimal module 173, prediction module 179, subscription
module 181, and vehicle failure prediction ("VFP") module 193.
Optimal module 173, in one embodiment, includes optimal module 176,
race car module 178, and normal condition module 180. It should be
noted that the underlying concept of the exemplary embodiment(s) of
the present invention would not change if one or more blocks
(modules or elements) were added to or removed from diagram
160.
[0069] Real-time information module 161, in one aspect, includes
mechanical data 162, external data 164, and internal data 166.
Mechanical data 162 is obtained by various sensors placed near or
on the mechanical devices. For example, sensors may be placed
around tire and/or wheel to obtain real-time information relating
to tire slippage, wheel slippage, unusual noise, ABS (anti-skid
braking system) deployment, and the like. Similarly, sensors can be
placed around engine to read engine temperature, vibration, sound,
and so on. In one aspect, mechanical data 162 includes readings or
measurements of temperature, humidity, vibration, sound/noise, and
the like.
[0070] External data 164, in one example, includes real-time
information relating to read condition, weather condition, traffic
congestions, nearby cars, traveling directions, and geographic
locations of the vehicle. External data 164 are obtained and/or
collected by exterior sensors mounted on the vehicle. Internal or
interior data 166 includes real-time readings about interior of
vehicle, such as, not limited to, driver, driver gaze, passengers,
smoking, drinking, texting, talking, and/or reading phone(s).
Interior data 166 are obtained and/or collected by interior sensors
and/or cameras. The component of current data 168, in one
embodiment, gathers real-time inputs from mechanical data 162,
external data 164, and internal data 166. The real-time inputs are
categorized, sorted, and organized whereby relevant current data is
stored while irrelevant data is discarded.
[0071] Historical data 170, in one example, is the relevant data
for the vehicle over a period of time. Historical data 170 can be
stored in the cloud data. Alternatively, historical data 170 is
stored locally. Also, a portion of historical data 170 is stored in
the cloud while a portion of historical data 170 is embedded in the
vehicle storage or VOC. A function of historical data 170 can
provide a quick examination as to whether the current real-time
data is normal or not in comparison with the historical data.
[0072] Cloud big data 171 includes samples 174 and big data 172
wherein cloud big data 171 is resided in the cloud or cloud
computing. Big data 172, in aspect, include a large number of
samples 174 that obtain from a large pool of vehicles having
similar attributes as the targeted vehicle. The attributes include,
but not limited to, similar cars, similar total mileage, similar
year produced, similar geographic location, similar features,
similar mechanical functions, similar maintenance records, similar
drivers, et cetera. An advantage of using the big data is that it
may contain information having similar problems or failures for
cars like the targeted vehicle. For example, big data 172 may
suggest to vehicle owner to take the vehicle to the dealer because
many similar cars like the vehicle have overheating problems.
[0073] Optimal module 173, in one embodiment, includes an optimal
condition 176, race car condition 178, and normal condition 180.
After receiving and evaluating inputs from current data 168,
historical data 170, and big data 172, optimal condition 176, based
on a set of predefined rules, examines and determines whether the
vehicle or targeted vehicle is in its optimal condition. In one
example, the optimal condition means the vehicle meets performance
requirements as manufactured and shows no sign of wear and tear. If
the optimal condition is met, the process proceeds to race car
condition 178. If the optimal condition fails to meet, the process
proceeds to normal condition 180 since certain level of wear and
tear can be normal. Race car condition 178, in one aspect, provides
information as to whether the vehicle is used for race or just
normal ordinary use. Normal condition 180 provides information as
to whether the current condition is normal wear and tear, or
whether the vehicle is about to break down or fail.
[0074] Predication module 179 includes four (4) predictions 182-188
wherein prediction 1 receives signal from race car condition 178
indicating the vehicle is used for car race. Based on a set of
predefined rules for car race criteria, a prediction report is
generated indicating various AIA indications. For example, AIA
indications can show where and when the tire slipped while the
vehicle was traveling at certain speed. Based on the design of the
vehicle and the road condition, the driver may have made some
mistakes. Such report is subsequently forwarded to subscription
module 181 indicating driver skills and/or mistakes as indicated by
numeral 190.
[0075] Prediction 2 receives signals from race car condition 178
indicating the vehicle is not used for car race. Based on a set of
predefined non-race car rules, a prediction report is generated
indicating various AIA indications which is subsequently sent to
subscription module 181 such as manufacture as a subscriber. For
example, based on the report, the manufacture can better understand
the quality of its vehicles as well as shortcomings.
[0076] Prediction 3 receives signal from normal condition 180
indicating the vehicle is normal even though some wears and tears
are considered normal. Based on a set of predefined rules for
normal criteria, a prediction report is generated indicating
various AIA indications indicating that certain wears and tears are
normal and predicts the time frame that the vehicle is likely to
require maintenance. For example, AIA indications can show where
and when the tire slipped while the vehicle was traveling at
certain speed. Based on the design of the vehicle and the road
condition, certain parts such as tire(s) may require replacement at
a predicted distance future. For example, the prediction reports
may suggest to subscriber 192 that the tires need to be replaced in
three months.
[0077] Prediction 4 receives signal from normal condition 180
indicating the vehicle is not normal and certain mechanical parts
may require services. Based on a set of predefined rules for VFP
criteria, a prediction report is generated indicating various AIA
indications indicating that one or more parts is about to fail. The
process proceeds to VFP to handle the failures.
[0078] Subscription module 181 includes driver 190, manufacture
191, and some subscribers 192 which includes owner 194, repair
shops 196, authority 198, police department, bus companies,
tracking companies, delivery companies, and so on. Authority 198
can be department of transportation which, for instance, wants to
know the safety features of electrical cars. The prediction report
can be helpful to DOT to assess whether certain vehicle should be
on the road or not.
[0079] An advantage of using VMP is that the vehicle is capable of
initiating feedback to subscribers such as driver or manufactures
or insurance company regarding the vehicle condition as well as
vehicle safety using its AIA system. For example, the feedback can
help driver skill, manufactures, insurance rates, road safety,
repair services, and automatic maintenance scheduling.
[0080] FIG. 1F is a logic diagram 2000 illustrating an AIA system
facilitated configured to provide a vehicle failure prediction
("VFP") via a virtuous cycle in accordance with one embodiment of
the present invention. Diagram 2000 includes inputs 2002,
predictive failure analyzer 2193, prediction 2006, and various
subscribers. Possible failure 193, which is the same or similar to
VFP 193 shown in FIG. 1E, triggers a process of predictive failure
analyzer 2193 since VMP indicates a possible failure(s). It should
be noted that the underlying concept of the exemplary embodiment(s)
of the present invention would not change if one or more blocks
(modules or elements) were added to or removed from diagram
2000.
[0081] Input 2002 includes current data 168, historical data 170,
and big data 2172 wherein big data 2172 further connected to
simples for similar vehicle 2008, samples for similar failure 2010,
recalls for similar vehicle 2012, and samples for similar mileage
2014. It should be noted that additional samples may be taken by
big data 2172. Big data 2172, in aspect, include a large number of
samples across a large geographic area containing a large pool of
vehicles having similar attributes as the targeted vehicle. The
attributes include, but not limited to, similar cars, similar total
mileage, similar year produced, similar geographic location,
similar features, similar mechanical functions, similar maintenance
records, similar drivers, similar failures, similar recalls,
similar reports, et cetera.
[0082] Based on the real-time data from current data 168,
historical collected information associated with the vehicle from
historical data 170, and big data 2172, predicative failure
analyzer 2193, based on a set of predefined failure rules,
generates predictions 2006. In one embodiment, predictive failure
analyzer 2193 includes multiple AI models trained by virtuous
cycles. For example, a failure predictive AI model is configured to
assessing a likelihood failure rate as well as failure time based
on current data, historical data, and big data.
[0083] Predications 2006 includes owner report 2020, manufacture
report 2030, government agency report 2040, and repair shop report
2050. For instance, owner report 2020 is a summary status report
for the vehicle owner(s) 2022 indicating the likelihood failure in
near future. The report, in one embodiment, is configured to
provide recommendations as repairable versus trade-in
assessment.
[0084] Manufacture report 2030 is a summary status report for the
vehicle manufacture(s) 2032 indicating when and where is the
failure. For example, such status report can indicate a nationwide
recall for fixing the problem(s). Also, the failure predictive AI
model may suggest improvements for the new vehicles based on
current data, historical data, and big data.
[0085] Government agency report 2040 is a summary status report for
the government agency 2042 such as department of motor vehicle
indicating whether such vehicle should be allowed to drive on the
public road. For example, a newly designed electrical vehicle may
be fire hazards if it reaches certain speed. Assessing a license
fee for such vehicle can be different from other traditional
vehicles. Also, the failure predictive AI model may suggest
improvements to the department of motor vehicle before a more
reasonable licensing fee is assessed.
[0086] Repair shop report 2050 is a summary status report for the
repair scheduling block 2052 indicating when and where the vehicle
will like to fail and when the vehicle should have a repair
performed to avoid the failure. For example, when a dealer obtains
such status report, it schedules an appointment with the driver or
owner to repair the defects or failure before the vehicle stops
working. In one aspect, the failure predictive AI model may
automatically schedule an appointment with the owner to an
available dealer or repair shop based on current data, historical
data, and big data.
[0087] FIG. 1G is a logic diagram illustrating an AIA process 3000
facilitated by AIA system configured to provide VMP using big data
via a virtuous cycle in accordance with one embodiment of the
present invention. At block 3160, the car or vehicle starts to move
driving by a driver or operator. After starting the vehicle, the
VOC of vehicle, at block 3161, activates various sensors at block
3186, and retrieves local data at block 3162. The local data, for
example, includes last driver's ID, stored driver's fingerprint,
and last known vehicle location before the vehicle stops. At block
3165, the driver ID is identified based on the inputs from local
data at block 3162 and images detected by sensors at block 3186. At
block 3168, the AIA process is able to determine whether an
expected driver is verified. If the driver is the expected driver,
the process proceeds to block 3171. If the driver is not the
expected driver, the process proceeds to block 3169 for further
identification. Upon identifying the driver, the driver's
information is updated at block 3170. For example, the driver is
not the same driver before the car or vehicle stops. It should be
noted that the identification process can use the AIA system
supported by the virtuous cycle.
[0088] At block 3166, various interior and exterior images, audio
sound, and internal mechanical conditions are detected based on the
input data from local data at block 3162 and sensors at block 3186.
At block 3167, location of the vehicle and moving direction of the
vehicle can be identified based on the geodata such as GPS (global
positioning system) at block 3163, local data from block 3162, and
images captured by sensors at block 3186. After collecting data
from blocks 3166-3168, the process proceeds from block 3171 to
block 3173. At block 3173, the process generates current data for
VMP associated with the targeted vehicle according to collected
data as well as a set of rules which can be obtained from block
3172. The current data is subsequently uploaded at block 3174.
[0089] At block 3176, the AIA process performs AI analysis based on
the current data from block 3174, historical data from block 3175,
and big data from block 3164. Based on the AI analysis, the process
provides a prediction at block 3178. Depending on the subscribers
at block 3177, various subscribers 3180-3182 including manufacture
3180 will receive the report relating to the prediction. It should
be noted that historical data update module at block 3179 is used
to update the historical data based on the current data.
[0090] Note that the expected driver can be, but not limited to,
company driver, fleet driver, bus drivers, self-driving vehicles,
drones, and the like. It should be noted that the underlying
concept of the exemplary logic diagram 3000 showing one
embodiment(s) of the present invention would not change if one or
more blocks (components or elements) were added to or removed from
diagram 3000.
[0091] FIGS. 2A-2B are block diagrams 200 illustrating a virtuous
cycle capable of facilitating AIAS using IA model in accordance
with one embodiment of the present invention. Diagram 200, which is
similar to diagram 100 shown in FIG. 1A, includes a containerized
sensor network 206, real-world scale data 202, and continuous
machine learning 204. In one embodiment, continuous machine
learning 204 pushes real-time models to containerized sensor
network 206 as indicated by numeral 210. Containerized sensor
network 206 continuously feeds captured data or images to
real-world scale data 202 with uploading in real-time or in a
batched format. Real-world scale data 202 provides labeled data to
continuous machine learning 204 for constant model training as
indicated by numeral 212. It should be noted that the underlying
concept of the exemplary embodiment(s) of the present invention
would not change if one or more blocks (or elements) were added to
or removed from FIG. 2A.
[0092] The virtuous cycle illustrated in diagram 200, in one
embodiment, is configured to implement IAS wherein containerized
sensor network 206 is similar to vehicle 102 as shown in FIG. 1A
and real-world scale data 202 is similar to CBN 104 shown in FIG.
1A. Also, continuous machine learning 204 is similar to MCL 106
shown in FIG. 1A. In one aspect, containerized sensor network 206
such as an automobile or car contains a containerized sensing
device capable of collecting surrounding information or images
using onboard sensors or sensor network when the car is in motion.
Based on the IA model, selective recording the collected
surrounding information is selectively recorded to a local storage
or memory.
[0093] Real-world scale data 202, such as cloud or CBN, which is
wirelessly coupled to the containerized sensing device, is able to
correlate with cloud data and recently obtained IA data for
producing labeled data. For example, real-world scale data 202
generates IA labeled data based on historical IA cloud data and the
surrounding information sent from the containerized sensing
device.
[0094] Continuous machine learning 204, such as MLC or cloud, is
configured to train and improve IA model based on the labeled data
from real-world scale data 202. With continuous gathering data and
training IA model(s), the IAS will be able to learn, obtain, and/or
collect all available IAs for the population samples.
[0095] In one embodiment, a virtuous cycle includes partition-able
Machine Learning networks, training partitioned networks,
partitioning a network using sub-modules, and composing partitioned
networks. For example, a virtuous cycle involves data gathering
from a device, creating intelligent behaviors from the data, and
deploying the intelligence. In one example, partition idea includes
knowing the age of a driver which could place or partition
"dangerous driving" into multiple models and selectively deployed
by an "age detector." An advantage of using such partitioned models
is that models should be able to perform a better job of
recognition with the same resources because the domain of discourse
is now smaller. Note that, even if some behaviors overlap by age,
the partitioned models can have common recognition components.
[0096] It should be noted that more context information collected,
a better job of recognition can be generated. For example,
"dangerous driving" can be further partitioned by weather
condition, time of day, traffic conditions, et cetera. In the
"dangerous driving" scenario, categories of dangerous driving can
be partitioned into "inattention", "aggressive driving", "following
too closely", "swerving", "driving too slowly", "frequent
breaking", deceleration, ABS event, et cetera.
[0097] For example, by resisting a steering behavior that is
erratic, the car gives the driver direct feedback on their behavior
- if the resistance is modest enough then if the steering behavior
is intentional (such as trying to avoid running over a small
animal) then the driver is still able to perform their irregular
action. However, if the driver is texting or inebriated then the
correction may alert them to their behavior and get their
attention. Similarly, someone engaged in "road rage" who is driving
too close to another car may feel resistance on the gas pedal. A
benefit of using IAS is to identify consequences of a driver's
"dangerous behavior" as opposed to recognizing the causes (texting,
etc.). The Machine Intelligence should recognize the causes as part
of the analysis for offering corrective action.
[0098] In one aspect, a model such as IA model includes some
individual blocks that are trained in isolation to the larger
problem (e.g. weather detection, traffic detection, road type,
etc.). Combining the blocks can produce a larger model. Note that
the sample data may include behaviors that are clearly bad (ABS
event, rapid deceleration, midline crossing, being too close to the
car in front, etc.). In one embodiment, one or more sub-modules are
built. The models include weather condition detection and traffic
detection for additional modules intelligence, such as "correction
vectors" for "dangerous driving."
[0099] An advantage of using a virtuous cycle is that it can learn
and detect object such as IA in the real world.
[0100] FIG. 2B is a block diagram 230 illustrating an alternative
exemplary virtuous cycle capable of detecting IA in accordance with
one embodiment of the present invention. Diagram 230 includes
external data source 234, sensors 238, crowdsourcing 233, and
intelligent model 239. In one aspect, components/activities above
dotted line 231 are operated in cloud 232, also known as in-cloud
component. Components/activities below dotted line 231 are operated
in car 236, also known as in-device or in-car component. It should
be noted that the underlying concept of the exemplary embodiment(s)
of the present invention would not change if one or more blocks (or
elements) were added to or removed from FIG. 2B.
[0101] In one aspect, in-cloud components and in-device components
coordinate to perform desirable user specific tasks. While in-cloud
component leverages massive scale to process incoming device
information, cloud applications leverage crowd sourced data to
produce applications. External data sources can be used to
contextualize the applications to facilitate intellectual
crowdsourcing. For example, in-car (or in-phone or in-device)
portion of the virtuous cycle pushes intelligent data gathering to
the edge application. In one example, edge applications can perform
intelligent data gathering as well as intelligent in-car
processing. It should be noted that the amount of data gathering
may rely on sensor data as well as intelligent models which can be
loaded to the edge.
[0102] FIG. 3 is a block diagram illustrating a cloud based network
using crowdsourcing approach to improve IA model(s) for AIAS in
accordance with one embodiment of the present invention. Diagram
300 includes population of vehicles 302, sample population 304,
model deployment 306, correlation component 308, and cloud
application 312. It should be noted that the underlying concept of
the exemplary embodiment(s) of the present invention would not
change if one or more blocks (or samples) were added to or removed
from FIG. 3.
[0103] Crowdsourcing is a process of using various sourcing or
specific models generated or contributed from other cloud or
Internet users for achieving needed services. For example,
crowdsourcing relies on the availability of a large population of
vehicles, phones, or other devices to source data 302. For example,
a subset of available devices such as sample 304 is chosen by some
criterion such as location to perform data gathering tasks. To
gather data more efficiently, intelligent models are deployed to a
limited number of vehicles 306 for reducing the need of large
uploading and processing a great deal of data in the cloud. It
should be noted that the chosen devices such as cars 306 monitor
the environment with the intelligent model and create succinct data
about what has been observed. The data generated by the intelligent
models is uploaded to the correlated data store as indicated by
numeral 308. It should be noted that the uploading can be performed
in real-time for certain information or at a later time for other
types of information depending on the need as well as condition of
network traffic.
[0104] Correlated component 308 includes correlated data storage
capable of providing a mechanism for storing and querying uploaded
data. Cloud applications 312, in one embodiment, leverage the
correlated data to produce new intelligent models, create crowd
sourced applications, and other types of analysis.
[0105] FIG. 4 is a block diagram 400 illustrating an IA model or
AIA system using the virtuous cycle in accordance with one
embodiment of the present invention. Diagram 400 includes a
correlated data store 402, machine learning framework 404, and
sensor network 406. Correlated data store 402, machine learning
framework 404, and sensor network 406 are coupled by connections
410-416 to form a virtuous cycle as indicated by numeral 420. It
should be noted that the underlying concept of the exemplary
embodiment(s) of the present invention would not change if one or
more blocks (circuit or elements) were added to or removed from
FIG. 4.
[0106] In one embodiment, correlated data store 402 manages
real-time streams of data in such a way that correlations between
the data are preserved. Sensor network 406 represents the
collection of vehicles, phones, stationary sensors, and other
devices, and is capable of uploading real-time events into
correlated data store 402 via a wireless communication network 412
in real-time or in a batched format. In one aspect, stationary
sensors include, but not limited to, municipal cameras, webcams in
offices and buildings, parking lot cameras, security cameras, and
traffic cams capable of collecting real-time images.
[0107] The stationary cameras such as municipal cameras and webcams
in offices are usually configured to point to streets, buildings,
parking lots wherein the images captured by such stationary cameras
can be used for accurate labeling. To fuse between motion images
captured by vehicles and still images captured by stationary
cameras can track object(s) such as car(s) more accurately.
Combining or fusing stationary sensors and vehicle sensors can
provide both labeling data and historical stationary sampling data
also known as stationary "fabric". It should be noted that during
the crowdsourcing applications, fusing stationary data (e.g.
stationary cameras can collect vehicle speed and position) with
real-time moving images can improve ML process.
[0108] Machine Learning ("ML") framework 404 manages sensor network
406 and provides mechanisms for analysis and training of ML models.
ML framework 404 draws data from correlated data store 402 via a
communication network 410 for the purpose of training modes and/or
labeled data analysis. ML framework 404 can deploy data gathering
modules to gather specific data as well as deploy ML models based
on the previously gathered data. The data upload, training, and
model deployment cycle can be continuous to enable continuous
improvement of models.
[0109] FIG. 5 is a block diagram 500 illustrating an exemplary
process of correlating data for AIAS in accordance with one
embodiment of the present invention. Diagram 500 includes source
input 504, real-time data management 508, history store 510, and
crowd sourced applications 512-516. In one example, source input
504 includes cars, phones, tablets, watches, computers, and the
like capable of collecting massive amount of data or images which
will be passed onto real-time data management 508 as indicated by
numeral 506. It should be noted that the underlying concept of the
exemplary embodiment(s) of the present invention would not change
if one or more blocks (or elements) were added to or removed from
FIG. 5.
[0110] In one aspect, a correlated system includes a real-time
portion and a batch/historical portion. The real-time part aims to
leverage new data in near or approximately real-time. Real-time
component or management 508 is configured to manage a massive
amount of influx data 506 coming from cars, phones, and other
devices 504. In one aspect, after ingesting data in real-time,
real-time data management 508 transmits processed data in bulk to
the batch/historical store 510 as well as routes the data to crowd
sourced applications 512-516 in real-time.
[0111] Crowd sourced applications 512-516, in one embodiment,
leverage real-time events to track, analyze, and store information
that can be offered to user, clients, and/or subscribers.
Batch-Historical side of correlated data store 510 maintains a
historical record of potentially all events consumed by the
real-time framework. In one example, historical data can be
gathered from the real-time stream and it can be stored in a
history store 510 that provides high performance, low cost, and
durable storage. In one aspect, real-time data management 508 and
history store 510 coupled by a connection 502 are configured to
perform IA data correlation as indicated by dotted line.
[0112] FIG. 6 is a block diagram illustrating an exemplary process
of real-time data management for AI model used for AIAS in
accordance with one embodiment of the present invention. Diagram
600 includes data input 602, gateway 606, normalizer 608, queue
610, dispatcher 616, storage conversion 620, and historical data
storage 624. The process of real-time data management further
includes a component 614 for publish and subscribe. It should be
noted that the underlying concept of the exemplary embodiment(s) of
the present invention would not change if one or more blocks
(circuit or elements) were added to or removed from FIG. 6.
[0113] The real-time data management, in one embodiment, is able to
handle large numbers (i.e., 10's of millions) of report events to
the cloud as indicated by numeral 604. API (application program
interface) gateway 606 can handle multiple functions such as client
authentication and load balancing of events pushed into the cloud.
The real-time data management can leverage standard HTTP protocols.
The events are routed to stateless servers for performing data
scrubbing and normalization as indicated by numeral 608. The events
from multiple sources 602 are aggregated together into a
scalable/durable/consistent queue as indicated by numeral 610. An
event dispatcher 616 provides a publish/subscribe model for crowd
source applications 618 which enables each application to look at a
small subset of the event types. The heterogeneous event stream,
for example, is captured and converted to files for long-term
storage as indicated by numeral 620. Long-term storage 624 provides
a scalable and durable repository for historical data.
[0114] FIG. 7 is a block diagram 700 illustrating a crowd sourced
application model for AI model for AIAS in accordance with one
embodiment of the present invention. Diagram 700 includes a gateway
702, event handler 704, state cache 706, state store 708, client
request handler 710, gateway 712, and source input 714. In one
example, gateway 702 receives an event stream from an event
dispatcher and API gateway 712 receives information/data from input
source 714. It should be noted that the underlying concept of the
exemplary embodiment(s) of the present invention would not change
if one or more blocks (or elements) were added to or removed from
FIG. 7.
[0115] The crowd sourced application model, in one embodiment,
facilitates events to be routed to a crowd source application from
a real-time data manager. In one example, the events enter gateway
702 using a simple push call. Note that multiple events are handled
by one or more servers. The events, in one aspect, are converted
into inserts or modifications to a common state store. State store
708 is able to hold data from multiple applications and is scalable
and durable. For example, State store 708, besides historical data,
is configured to store present data, information about "future
data", and/or data that can be shared across applications such as
predictive AI (artificial intelligence).
[0116] State cache 706, in one example, is used to provide fast
access to commonly requested data stored in state store 708. Note
that application can be used by clients. API gateway 712 provides
authentication and load balancing. Client request handler 710
leverages state store 708 for providing client data.
[0117] In an exemplary embodiment, an onboard IA model is able to
handle real-time IA detection based on triggering events. For
example, after ML models or IA models for IA detection have been
deployed to all or most of the vehicles, the deployed ML models
will report to collected data indicating IAS for facilitating
issuance of real-time warning for dangerous event(s). The
information or data relating to the real-time dangerous event(s) or
IAS is stored in state store 708. Vehicles 714 looking for IA
detection can, for example, access the IAS using gateway 712.
[0118] FIG. 8 is a block diagram 800 illustrating a method of
storing AI related data using a geo-spatial objective storage for
AIAS in accordance with one embodiment of the present invention.
Diagram 800 includes gateway 802, initial object 804, put call 806,
find call 808, get call 810, SQL (Structured Query Language) 812,
non-SQL 814, and geo-spatial object storage 820. It should be noted
that the underlying concept of the exemplary embodiment(s) of the
present invention would not change if one or more blocks (circuit
or elements) were added to or removed from FIG. 8.
[0119] Geo-spatial object storage 820, in one aspect, stores or
holds objects which may include time period, spatial extent,
ancillary information, and optional linked file. In one embodiment,
geo-spatial object storage 820 includes UUID (universally unique
identifier) 822, version 824, start and end time 826, bounding 828,
properties 830, data 832, and file-path 834. For example, while
UUID 822 identifies an object, all objects have version(s) 824 that
allow schema to change in the future. Start and end time 826
indicates an optional time period with a start time and an end
time. An optional bounding geometry 828 is used to specify spatial
extent of an object. An optional set of properties 830 is used to
specify name-value pairs. Data 832 can be binary data. An optional
file path 834 may be used to associate with the object of a file
containing relevant information such as MPEG (Moving Picture
Experts Group) stream.
[0120] In one embodiment, API gateway 802 is used to provide access
to the service. Before an object can be added to the store, the
object is assigned an UUID which is provided by the initial object
call. Once UUID is established for a new object, the put call 804
stores the object state. The state is stored durably in Non-SQL
store 814 along with UUID. A portion of UUID is used as hash
partition for scale-out. The indexable properties includes version,
time duration, bounding, and properties which are inserted in a
scalable SQL store 812 for indexing. The Non-SQL store 814 is used
to contain the full object state. Non-SQL store 814 is scaled-out
using UUID as, for example, a partition key.
[0121] SQL store 812 is used to create index tables that can be
used to perform queries. SQL store 812 may include three tables 816
containing information, bounding, and properties. For example,
information holds a primary key, objects void, creation timestamp,
state of object and object properties "version" and "time
duration." Bounding holds the bounding geometry from the object and
the id of the associated information table entry. Properties hold
property name/value pairs from the object stored as one name/value
pair per row along with ID of associated info table entry.
[0122] Find call 808, in one embodiment, accepts a query and
returns a result set, and issues a SQL query to SQL store 812 and
returns a result set containing UUID that matches the query.
[0123] FIG. 9 is a block diagram 900 illustrating an exemplary
approach of analysis engine analyzing collected data for AIAS in
accordance with one embodiment of the present invention. Diagram
900 includes history store 902, analysis engine 904, and
geo-spatial object store 906. It should be noted that the
underlying concept of the exemplary embodiment(s) of the present
invention would not change if one or more blocks (circuit or
elements) were added to or removed from FIG. 9.
[0124] In one aspect, diagram 900 illustrates analysis engine 904
containing ML training component capable of analyzing labeled data
based on real-time captured IA data and historical data. The data
transformation engine, in one example, interacts with Geo-spatial
object store 906 to locate relevant data and with history store to
process the data. Optimally, the transformed data may be
stored.
[0125] It should be noted that virtuous cycle employing ML training
component to provide continuous model training using real-time data
as well as historical samples, and deliver IA detection model for
one or more subscribers. A feature of virtuous cycle is able to
continuous training a model and able to provide a real-time or near
real-time result. It should be noted that the virtuous cycle is
applicable to various other fields, such as, but not limited to,
business intelligence, law enforcement, medical services, military
applications, and the like.
[0126] FIG. 10 is a block diagram 1000 illustrating an exemplary
containerized sensor network used for sensing information for AIAS
in accordance with one embodiment of the present invention. Diagram
1000 includes a sensor bus 1002, streaming pipeline 1004, and
application layer 1006 wherein sensor bus 1002 is able to receive
low-bandwidth sources and high-bandwidth sources. Streaming
pipeline 1004, in one embodiment, includes ML capable of generating
unique model such as model 1008. It should be noted that the
underlying concept of the exemplary embodiment(s) of the present
invention would not change if one or more blocks (circuit or
elements) were added to or removed from FIG. 10.
[0127] FIG. 11 is a block diagram 1100 illustrating a processing
device, VOC, and/or computer(s) which can be installed in a vehicle
to support onboard cameras, CAN (Controller Area Network) bus,
Inertial Measurement Units, Lidar, et cetera for facilitating
virtuous cycle in accordance with one embodiment of the present
invention. Computer system or IAS 1100 can include a processing
unit 1101, an interface bus 1112, and an input/output ("IO") unit
1120. Processing unit 1101 includes a processor 1102, a main memory
1104, a system bus 1111, a static memory device 1106, a bus control
unit 1105, I/O element 1130, and IAS element 1185. It should be
noted that the underlying concept of the exemplary embodiment(s) of
the present invention would not change if one or more blocks
(circuit or elements) were added to or removed from FIG. 11.
[0128] Bus 1111 is used to transmit information between various
components and processor 1102 for data processing. Processor 1102
may be any of a wide variety of general-purpose processors,
embedded processors, or microprocessors such as ARM.RTM. embedded
processors, Intel.RTM. Core.TM. Duo, Core.TM. Quad, Xeon.RTM.,
Pentium microprocessor, Motorola.TM. 68040, AMD.RTM. family
processors, or Power PC.TM. microprocessor.
[0129] Main memory 1104, which may include multiple levels of cache
memories, stores frequently used data and instructions. Main memory
1104 may be RAM (random access memory), MRAM (magnetic RAM), or
flash memory. Static memory 1106 may be a ROM (read-only memory),
which is coupled to bus 1111, for storing static information and/or
instructions. Bus control unit 1105 is coupled to buses 1111-1112
and controls which component, such as main memory 1104 or processor
1102, can use the bus. Bus control unit 1105 manages the
communications between bus 1111 and bus 1112.
[0130] I/O unit 1120, in one embodiment, includes a display 1121,
keyboard 1122, cursor control device 1123, and communication device
1125. Display device 1121 may be a liquid crystal device, cathode
ray tube ("CRT"), touch-screen display, or other suitable display
device. Display 1121 projects or displays images of a graphical
planning board. Keyboard 1122 may be a conventional alphanumeric
input device for communicating information between computer system
1100 and computer operator(s). Another type of user input device is
cursor control device 1123, such as a conventional mouse, touch
mouse, trackball, or other type of cursor for communicating
information between system 1100 and user(s).
[0131] IA element 1185, in one embodiment, is coupled to bus 1111,
and configured to interface with the virtuous cycle for
facilitating IA detection(s). For example, if system 1100 is
installed in a car, IA element 1185 is used to operate the IA model
as well as interface with the cloud based network. If system 1100
is placed at the cloud based network, IA element 1185 can be
configured to handle the correlating process for generating labeled
data. Communication device 1125 is coupled to bus 1111 for
accessing information from remote computers or servers, such as
server 104 or other computers, through wide-area network 102.
Communication device 1125 may include a modem or a network
interface device, or other similar devices that facilitate
communication between computer 1100 and the network. Computer
system 1100 may be coupled to a number of servers via a network
infrastructure such as the Internet.
[0132] The exemplary embodiment of the present invention includes
various processing steps, which will be described below. The steps
of the embodiment may be embodied in machine or computer executable
instructions. The instructions can be used to cause a general
purpose or special purpose system, which is programmed with the
instructions, to perform the steps of the exemplary embodiment of
the present invention. Alternatively, the steps of the exemplary
embodiment of the present invention may be performed by specific
hardware components that contain hard-wired logic for performing
the steps, or by any combination of programmed computer components
and custom hardware components.
[0133] FIG. 12 is a flowchart 1200 illustrating a process of AIAS
for providing a report of VMP predicting vehicle status in
accordance with one embodiment of the present invention. At block
1202, a process able to predict an event relating to machinal
performance using data obtained from interior and exterior sensors,
VOC, and cloud data activates interior and exterior sensors mounted
on a vehicle operated by a driver for obtaining current data
relating to external surroundings, interior settings, and internal
mechanical conditions of the vehicle. For example, after enabling a
set of outward facing cameras mounted on the vehicle for recording
external surrounding images representing a geographic environment,
one or more inward facing cameras mounted in the vehicle is
initiated for collecting interior images of the vehicle. Also, a
set of internal sensors attached to various mechanical components
is activated for measuring temperatures, functionalities, or audio
sounds associated with mechanical components within the vehicle.
The process, in one embodiment, is capable of detecting driver's
response time based on a set of identified road conditions and
information from a controller area network ("CAN") bus of the
vehicle. The real-time data relating to vehicle performance, road
condition, traffic congestion, and weather condition is
recorded.
[0134] At block 1204, the current data is forwarded to VOC for
generating a current vehicle status representing substantially
real-time vehicle performance in accordance with the current
data.
[0135] At block 1206, a historical data associated with the vehicle
including mechanical condition is retrieved. Note that the
historical data is updated in response to the current data.
[0136] At block 1208, a normal condition signal is issued when the
current vehicle status does not satisfy with optimal condition
based on the historical data. In one aspect, after uploading the
current vehicle status to a vehicle performance predictor which
resides at least partially at a cloud via a communications network,
the big data is obtained from the cloud wherein the big data
represents large car samples having similar attributes as the
vehicle. For example, the big data accumulates information from
cars with similar brands, similar mileages, similar years, similar
geographic location, and similar drivers. The current vehicle
status is compared with the big data and the historical data to
assess whether the vehicle operates in a normal condition.
[0137] At block 1210, a race car condition is issued when the
current vehicle status meets with the optimal condition based on
the historical data. In one example, the current vehicle status is
forwarded to a subscriber for evaluating driver's driving skill.
The current vehicle status can also be forwarded to a subscriber
for assessing normal wearing and tearing. Alternatively, a
subscriber schedules a maintenance or repair appointment with the
driver based on the current vehicle status. In one embodiment, a
manufacture can initiate a recall for automobiles similar to the
vehicle at least partially based on the current vehicle status.
[0138] While particular embodiments of the present invention have
been shown and described, it will be obvious to those of ordinary
skills in the art that based upon the teachings herein, changes and
modifications may be made without departing from this exemplary
embodiment(s) of the present invention and its broader aspects.
Therefore, the appended claims are intended to encompass within
their scope all such changes and modifications as are within the
true spirit and scope of this exemplary embodiment(s) of the
present invention.
* * * * *