U.S. patent application number 15/257487 was filed with the patent office on 2016-12-22 for dynamic routing intelligent vehicle enhancement system.
The applicant listed for this patent is Feeney Wireless, LLC. Invention is credited to Justin D. Bloom, Robert E. Ralston.
Application Number | 20160370199 15/257487 |
Document ID | / |
Family ID | 53400637 |
Filed Date | 2016-12-22 |
United States Patent
Application |
20160370199 |
Kind Code |
A1 |
Ralston; Robert E. ; et
al. |
December 22, 2016 |
DYNAMIC ROUTING INTELLIGENT VEHICLE ENHANCEMENT SYSTEM
Abstract
Embodiments of the invention provide a dynamic routing
intelligent vehicle enhancement system and method. Intelligent land
buoys can be proximately disposed to roadways. Each of the
intelligent land buoys can gather situational awareness information
about the roadways and one or more vehicles traveling thereon. The
intelligent land buoys can compress the situational awareness
information. One or more remote computer servers can receive the
compressed situational awareness information from the plurality of
intelligent land buoys, decompress it, and process the decompressed
situational awareness information. The one or more remote computer
servers can generate vehicle operational intelligence information
based at least on the decompressed situational awareness
information, and can transmit the vehicle operational intelligence
information to the plurality of intelligent land buoys and/or
directly to one or more autonomous or semi-autonomous vehicles.
Inventors: |
Ralston; Robert E.; (Eugene,
OR) ; Bloom; Justin D.; (Eugene, OR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Feeney Wireless, LLC |
Eugene |
OR |
US |
|
|
Family ID: |
53400637 |
Appl. No.: |
15/257487 |
Filed: |
September 6, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14134738 |
Dec 19, 2013 |
9435652 |
|
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15257487 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G08G 1/096844 20130101;
G01C 21/3492 20130101; G01C 21/26 20130101; G08G 1/0108 20130101;
G08G 1/096811 20130101; G08G 1/0116 20130101; G08G 1/0133 20130101;
G08G 1/096741 20130101; G08G 1/0145 20130101; G08G 1/0141 20130101;
G08G 1/096775 20130101; G08G 1/096783 20130101 |
International
Class: |
G01C 21/34 20060101
G01C021/34; G08G 1/0967 20060101 G08G001/0967; G08G 1/01 20060101
G08G001/01 |
Claims
1. A dynamic routing intelligent vehicle enhancement system,
comprising: a plurality of intelligent land buoys that are
proximately disposed to a plurality of roadways, each of the
intelligent land buoys being configured to gather situational
awareness information about the roadways and one or more vehicles
traveling thereon, and to compress the situational awareness
information; and one or more remote computer servers
communicatively coupled to the plurality of intelligent land buoys
and configured to receive the compressed situational awareness
information from the plurality of intelligent land buoys, to
decompress the situational awareness information, to process the
decompressed situational awareness information, to generate vehicle
operational intelligent information based at least on the
decompressed situational awareness information, and to transmit the
vehicle operational intelligence information to the plurality of
intelligent land buoys.
2. The dynamic routing intelligent vehicle enhancement system of
claim 1, wherein the intelligent land buoys are configured to
transmit a beacon including the vehicle operational intelligence
information to the one or vehicles.
3. The dynamic routing intelligent vehicle enhancement system of
claim 1, wherein the one or more vehicles include at least one of
an autonomous vehicle or a semi-autonomous vehicle.
4. The dynamic routing intelligent vehicle enhancement system of
claim 1, wherein the vehicle operational intelligence information
includes dynamic vehicle routing information.
5. The dynamic routing intelligent vehicle enhancement system of
claim 1, wherein the vehicle operational intelligence information
includes a predictive awareness alert indicating a time period
within which an occupant in a particular vehicle form among the one
or more vehicles is advised to take operational control of the
particular vehicle.
6. The dynamic routing intelligent vehicle enhancement system of
claim 1, wherein the plurality of intelligent land buoys are
configured to be located at an elevation that is higher than tops
of the one or more vehicles, and to transmit a beacon including the
vehicle operational intelligence information toward the one or more
vehicles.
7. The dynamic routing intelligent vehicle enhancement system of
claim 1, wherein each of the plurality of intelligent land buoys
further comprises: one or more short range radio transceivers
configured to receive, from the one or more vehicles, at least a
first portion of the situational awareness information about the
roadways and the one or more vehicles traveling thereon; a
processor configured to compress the first and second portions of
the situational awareness information and; one or more long range
radio transceivers configured to transmit, to the one or more
remote computer servers, the compressed situational awareness
information.
8. The dynamic routing intelligent vehicle enhancement system of
claim 7, wherein the first portion of the situational awareness
information includes at least one of a vehicle identification or a
service set identification (SSID).
9. The dynamic routing intelligent vehicle enhancement system of
claim 7, wherein: the first portion of the situational awareness
information includes a service set identification (SSID); and the
plurality of intelligent land buoys are configured to detect a
first location of a particular vehicle from among the one or more
vehicles associated with the SSID and to detect a second location
of the particular vehicle from among the one or more vehicles
associated with the SSID.
10. The dynamic routing intelligent vehicle enhancement system of
claim 9, wherein: the plurality of intelligent lad buoys are
configured to transmit the first location information and the
second location information to the one or more remote computer
servers; and; the one or more remote computer servers are
configured to determine a speed of the particular vehicle based at
least on the first location information and the second location
information.
11. The dynamic routing intelligent vehicle enhancement system of
claim 7, wherein the first portion of the situational awareness
information includes at least on e of road temperature, wheel
slippage information, or pothole presence information.
12. The dynamic routing intelligent vehicle enhancement system of
claim 7, wherein each of the plurality of intelligent land buoys
further comprises: one or more infrared cameras configured to
receive at least a third portion of the situational awareness
information about the roadways and the one or more vehicles
traveling thereon.
13. The dynamic routing intelligent vehicle enhancement system of
claim 12, wherein each of the plurality of intelligent land buoys
further comprises: one or more motion detectors configured to
receive at least a fourth portion of the situational awareness
information about the roadways and the one more vehicles traveling
thereon; and one or more temperature sensors configured to receive
at least a fifth portion of the situational awareness information
about the roadways and the one or more vehicles traveling
thereon.
14. The dynamic routing intelligent vehicle enhancement system of
claim 13, wherein each of the plurality of intelligent land buoys
is configured to receive at least a sixth portion of the
situational awareness information from at least one of an inductive
traffic sensor or a strain gauge.
15. The dynamic routing intelligent vehicle enhancement system of
claim 7, wherein each of the plurality of intelligent land buoys
further comprises: identification logic configured to identify,
based at least on the situational awareness information, the
presence of a traffic obstruction.
16. The dynamic routing intelligent vehicle enhancement system of
claim 15, wherein each of the plurality of intelligent land buoys
further comprises: tracking logic configured to track, based at
least on the situational awareness information, the presence,
position, and speed of the traffic obstruction.
17. The dynamic routing intelligent vehicle enhancement system of
claim 16, wherein the traffic obstruction includes at least one of
a vehicle, a pedestrian, an animal, road debris, or an object.
18. The dynamic routing intelligent vehicle enhancement system of
claim 1, wherein: the plurality of intelligent land buoys are
configured to gather the situational awareness information about
each of a plurality of route segments, and to compress the
situational awareness information about each of a plurality of
route segments, and to compress the situational awareness
information about each of the plurality of route segments into a
corresponding route segment impedance score; the plurality of
intelligent land buoys are configured to transmit the route segment
impedance scores to the one or more remote computer servers; the
one or more remote computer servers are configured to aggregate a
first portion of the route segment impedance scores associated with
a first route between a first geographic location and a second
geographic location, to aggregate a second portion of the route
segment impedance scores associated with a second route between the
first geographic location and the second geographic location, and
to aggregate N portions of the route segment impedance scores
associated with corresponding N routes between the first geographic
location and the second geographic location.
19. They dynamic routing intelligent vehicle enhancement system of
claim 18, wherein: the one or more remote computer servers are
configured to select from among the aggregated N portions a present
lowest aggregated impedance score associated with a particular one
route from among the N routes; and the one or more remote computer
servers are configured to transmit a present lowest impedance route
selection to the plurality of intelligent land buoys based at least
on the present lowest aggregated impedance score.
20. They dynamic routing intelligent vehicle enhancement system of
claim 18, wherein: the one or more remote computer servers are
configured to select from among the aggregated N portions a
predicted future lowest aggregated impedance score associated with
a particular one route from among the N routes, wherein the
predicted future score is based at least on a history of prior
impedance scores; and the one more remote computer servers are
configured to determine a predicted lowest impedance route
selection based at least on the predicted future lowest aggregated
impedance score, and to transmit the selection to the plurality of
intelligent land buoys.
21-25. (canceled)
Description
FIELD OF THE INVENTION
[0001] This application pertains to autonomous and/or
semi-autonomous vehicles, and more particularly, to a dynamic
muting intelligent vehicle enhancement system and related methods
for acquiring situational awareness information, and providing
operational intelligence to autonomous and/or semi-autonomous
vehicles for safe, efficient, and automated land navigation.
BACKGROUND
[0002] Conventionally, vehicles have been operated by humans. More
recent advances have focused on making vehicles operationally
autonomous or at least semi-autonomous. The expectation is that
safety can be increased by removing the element of human error,
which is the primary cause of accidents. But current approaches are
limited by the small and localized type of information available to
the vehicle, such as line of sight, immediate road obstructions,
misinterpretation of localized data (e.g., mistaking a pedestrian
for a stationary object), or the like. Since autonomous vehicles
must often make decisions having life-or-death implications to
humans, such a limited amount of low-quality information presents a
challenge, and is insufficient for a standard of safety that is
acceptable to society and governments at large.
[0003] Indeed, for advanced autonomous vehicle technology to take
root in the world, significant enhancements to safety must be
achieved. While automated systems can provide reliable operation
for some or most of the time, human intervention might be needed in
the event of system failure, particularly adverse weather
conditions, unexpected events, or other situations not anticipated
by the conventional technology. For example, a human might be
required to take control of the vehicle under peculiar situations
within a certain period of time, such as 10 seconds, 20 seconds, or
the like. Such requirements may be mandated by governments to
ensure a certain level of system quality and safety. However,
existing technology lacks the granular type of information needed
to provide an alert under such conditions, and thus, it fails to
achieve appropriate levels of safety.
[0004] In addition, conventional navigation technologies fail to
provide sufficient granular navigable information for autonomous
vehicles to truly operate with high levels of safety and
performance. The situation on the road is often dynamic and
frequently changes. Traffic jams can occur at various times of the
day. Accidents can introduce an element of temporary chaos. Road
obstructions can be dangerous. Roadways are inefficiently used.
Some routes are taken by drivers when other routes would be better.
Congestion, frustration, road rage, fuel inefficiencies, and the
like, are the undesirable result.
[0005] Accordingly, a need remains for a dynamic routing
intelligent vehicle enhancement system and related methods. A need
also remains for a smart grid to improve the utility and capacity
of existing roads. Embodiments of the invention address these and
other limitations in the prior art.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 illustrates a diagram of an example system for
acquiring situational awareness information and for providing
operational intelligence to autonomous and/or semi-autonomous
vehicles in accordance with various embodiments of the present
invention.
[0007] FIG. 2 illustrates a diagram of examples of situational
awareness information according to various embodiments.
[0008] FIG. 3 illustrates a diagram of examples of vehicle
operational intelligence information according to various
embodiments.
[0009] FIG. 4 illustrates a diagram of distributed intelligent land
buoys connected to one or more remote servers in accordance with
embodiments of the present invention.
[0010] FIG. 5 illustrates a diagram of interconnected intelligent
land buoys in accordance with embodiments of the present
invention.
[0011] FIG. 6 illustrates a diagram of an example system including
an intelligent land buoy interconnected with a vehicle, sensors,
remote servers, third party servers, and third party clients, in
accordance with embodiments of the present invention.
[0012] FIG. 7 illustrates a diagram of various example third party
clients in accordance with embodiments of the present
invention.
[0013] FIG. 8 illustrates a diagram of an intelligent land buoy
associated with a route segment having an obstruction impeding the
flow of traffic, in accordance with embodiments of the present
invention.
[0014] FIG. 9 illustrates a diagram of various kinds of alerts in
accordance with embodiments of the present invention.
[0015] FIG. 10A illustrates a diagram of an intelligent land buoy
associated with multiple route segments in accordance with
embodiments of the present invention.
[0016] FIG. 10B illustrates a table including example associations
between route segments and impedance scores in accordance with
embodiments of the present invention.
[0017] FIG. 10C illustrates a table including example associations
between complete routes and aggregated portions of route segment
impedance scores in accordance with embodiments of the present
invention.
[0018] FIG. 11 illustrates a diagram of an impedance function in
the time domain in accordance with embodiments of the present
invention.
[0019] FIG. 12 is a flow diagram illustrating a technique for
providing dynamic routing intelligent vehicle enhancement services
in accordance with embodiments of the present invention.
[0020] FIG. 13 is a flow diagram illustrating another technique for
providing dynamic routing intelligent vehicle enhancement services
in accordance with embodiments of the present invention.
[0021] The foregoing and other features of the invention will
become more readily apparent from the following detailed
description, which proceeds with reference to the accompanying
drawings.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0022] Reference will now be made in detail to embodiments of the
inventive concept, examples of which are illustrated in the
accompanying drawings. The accompanying drawings are not
necessarily drawn to scale. In the following detailed description,
numerous specific details are set forth to enable a thorough
understanding of the inventive concept. It should be understood,
however, that persons having ordinary skill in the art may practice
the inventive concept without these specific details. In other
instances, well-known methods, procedures, components, circuits,
and networks have not been described in detail so as not to
unnecessarily obscure aspects of the embodiments.
[0023] It will be understood that, although the terms first,
second, etc. may be used herein to describe various elements, these
elements should not be limited by these terms. These terms are only
used to distinguish one element from another. For example, a first
network could be termed a second network, and, similarly, a second
network could be termed a first network, without departing from the
scope of the inventive concept.
[0024] It will be understood that when an element or layer is
referred to as being "on," "coupled to" or "connected to" another
element or layer, it can be directly on, directly coupled to or
directly connected to the other element or layer, or intervening
elements or layers may be present. In contrast, when an element is
referred to as being "directly on," "directly coupled to" or
"directly connected to" another element or layer, there are no
intervening elements or layers present. Like numbers refer to like
elements throughout. As used herein, the term "and/or" includes any
and all combinations of one or more of the associated listed
items.
[0025] The terminology used in the description of the inventive
concept herein is for the purpose of describing particular
embodiments only and is not intended to be limiting of the
inventive concept. As used in the description of the inventive
concept and the appended claims, the singular forms "a", "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will also be understood
that the term "and/or" as used herein refers to and encompasses any
and all possible combinations of one or more of the associated
listed items. It will be further understood that the terms
"comprises" and/or "comprising," when used in this specification,
specify the presence of stated features, integers, steps,
operations, elements, and/or components, but do not preclude the
presence or addition of one or more other features, integers,
steps, operations, elements, components, and/or groups thereof.
[0026] FIG. 1 illustrates a diagram 100 of an example system for
acquiring situational awareness information and for providing
operational intelligence to autonomous and/or semi-autonomous
vehicles in accordance with various embodiments of the present
invention.
[0027] Intelligent land buoys (e.g., 105) can be proximately
disposed to a plurality of roadways (e.g., 110). The term
"roadways" can include roads, intersections, bridges, railways,
rail crossings, or the like. Each of the intelligent land buoys 105
can gather situational awareness information (e.g., 125) about one
or more route segments of the roadways 110, and about one or more
vehicles (e.g., 115) traveling thereon. In some embodiments, the
intelligent land buoys 105 can receive situational awareness
information 125 directly from the one or more vehicles 115. The
situational awareness information 125 can include information about
the present state of reality as it relates to associated vehicle
route segments, adjacent areas, and/or vehicles. Examples of the
situational awareness information 125 are further described in
detail below.
[0028] The intelligent land buoys 105 can compress and/or store the
situational awareness information 125, the compression technique of
which is also further described in detail below. One or more remote
computer servers (e.g., 120) can be communicatively coupled to the
intelligent land buoys 105 and can receive the compressed
situational awareness information 125 from the plurality of
intelligent land buoys 105. The one or more remote computer servers
120 can be distributed and/or clustered. The one or more remote
computer servers 120 can decompress the situational awareness
information 125, process the decompressed situational awareness
information 125, and/or store the decompressed situational
awareness information 125. Consequently, the one or more remote
computer servers 120 can generate vehicle operational intelligence
information 130 based at least on the decompressed situational
awareness information 125, as also further described in detail
below.
[0029] The one or more remote computer servers 120 can transmit the
vehicle operational intelligence information 130 to the plurality
of intelligent land buoys 105. One or more of the intelligent land
buoys 105 may in turn transmit the operational intelligence
information 130 to the one or more vehicles 115. In addition or
alternatively, the one or more remote computer servers 120 can
directly transmit the vehicle operational intelligence information
130 to the one or more vehicles 115. In other words, a map service
and/or navigation device associated with the vehicle or provided
within the vehicle can receive the operational intelligence
information 130 so that it can have access to real-time and highly
accurate operational information.
[0030] The intelligent land buoys 105 can be configured to transmit
a beacon including the vehicle operational intelligence information
130 to the one or more vehicles 115. The beacon may be periodically
transmitted. In addition or alternatively, the beacon may be
continually transmitted. The plurality of intelligent land buoys
105 can be located at an elevation that is higher than tops of the
one or more vehicles 115, and can transmit the beacon including the
vehicle operational intelligence information 130 toward the one or
more vehicles 115. For example, each of the intelligent land buoys
105 can be attached to a utility pole 135, a traffic signal pole or
apparatus 145, a building 140, a hovering airborne drone 150, a
radio tower (not shown), or the like.
[0031] The one or more vehicles 115 can include an autonomous
vehicle and/or a semi-autonomous vehicle. The vehicle can be an
automobile for primarily transporting people. In addition, the
vehicle can be other suitable types of vehicles for transporting
goods, people, or any combination thereof. For example, the vehicle
can be a car, a truck, a train, or the like. An autonomous vehicle
incorporates artificial intelligence. For example, an autonomous
vehicle can automatically navigate and operate the vehicle itself
with little to no assistance from a human operator. A
semi-autonomous vehicle also incorporates artificial intelligence,
but not to the same degree as the autonomous vehicle. In other
words, a semi-autonomous vehicle may require some assistance or
operational control from a human. For the purposes of this
disclosure, when referring to a "vehicle" or "vehicles," such
vehicle or vehicles can be autonomous, semi-autonomous, or any
combination thereof.
[0032] The vehicle operational intelligence 130 can include dynamic
vehicle routing information. For example, the vehicle operational
intelligence 130 can assist a vehicle to navigate from a first
geographic location to a second geographic location. The geographic
locations can be within a neighborhood 155, in a parking lot 160,
near a building 140, on a road such as highway 110, or the like. In
addition, the vehicle operational intelligence 130 can include a
predictive awareness alert indicating a time period within which an
occupant in a particular vehicle from among the one or more
vehicles is advised to take operational control of the particular
vehicle, as further described in detail below.
[0033] Each of the intelligent land buoys 105 can include one or
more short range radio transceivers (e.g., 165) to receive, from
the one or more vehicles 115, at least a first portion of the
situational awareness information 125 about the roadways 110 and
the one or more vehicles 115 traveling thereon. In addition, each
of the intelligent land buoys 105 can include one or more visible
light cameras (e.g., 170) to receive at least a second portion of
the situational awareness information 125 about the roadways 110
and the one or more vehicles 115 traveling thereon. Moreover, each
of the intelligent land buoys 105 can include one or more long
range radio transceivers (e.g., 175) to transmit, to the one or
more remote computer servers 120, the compressed situational
awareness information 125. Additional details of additional and
various components of the intelligent land buoys 105 are provided
below.
[0034] FIG. 2 illustrates a diagram of the examples of the
situational awareness information 125 (of FIG. 1) gathered by the
intelligent land buoys 105 (of FIG. 1) according to various
embodiments. The situational awareness information 125 can include,
for example, a vehicle identification (ID) 200, a service set
identification (SSID) 202 associated with a mobile phone or other
mobile device, road temperature 204, wheel slippage information
206, pothole and/or gravel presence and location information 208,
visible light camera information 210, infrared camera information
212, motion detector information 214, temperature sensor
information 216, inductive traffic sensor information 218, strain
gauge information 220, traffic density information 222, traffic
obstruction information 224, fog and/or rain density information
226, weather status information 228, detected debris information
230, ice presence information 232, impaired vision information 234,
environmental information 236, road or tunnel construction or
maintenance information 238, pedestrian density information 240,
wild life detection information 242, or the like.
[0035] The intelligent land buoys 105 (of FIG. 1) can gather and
process the situational awareness information 125 and react to it
in real-time. For example, one or more of the intelligent land
buoys 105 can detect a first location of a particular vehicle from
among the one or more vehicles 115, and to detect a second location
of the particular vehicle from among the one or more vehicles 115.
In this manner, the location, distance traveled, speed, and so
forth, of the one or more vehicles 115 can be determined. Such
determination can be made by the intelligent land buoys 105 by
tracking SSIDs that are broadcast from the vehicles, MAC addresses,
or other suitable vehicle identifiers. Moreover, the intelligent
land buoys 105 can use machine vision techniques to make such
determinations. The SSID or other identifiers need not reveal any
personal or private information about the vehicle or its
occupants.
[0036] By way of a more specific example, one or more of the
intelligent land buoys 105 can detect a first location of a
particular vehicle associated with a particular SSID from among the
one or more vehicles 115, and to detect a second location of the
particular vehicle associated with the particular SSID from among
the one or more vehicles 115, and thus, the location, distance
traveled, speed, etc., can be determined.
[0037] In addition or alternatively, the intelligent land buoys 105
can transmit the first location information and the second location
information to the one or more remote computer servers 120 (of FIG.
1), and the one or more remote computer servers 120 can determine
the location, distance traveled, speed, and so forth, of the
particular vehicle based at least on the first location information
and the second location information.
[0038] The intelligent land buoys 105 can compress, store, and/or
transmit the gathered situational awareness information 125 to the
one or more remote servers 120 (of FIG. 1). The types of
compression are described in detail below. The one or more remote
servers 120 can receive, de-compress, and process the situation
awareness information 125, and provide the operational intelligence
information 130 in real-time or near real-time to the intelligent
land buoys 105.
[0039] FIG. 3 illustrates a diagram of examples of the vehicle
operational intelligence information 130 (of FIG. 1) according to
various embodiments. The vehicle operational intelligence
information 130 can include, for example, dynamic vehicle routing
information 300, predictive awareness alerts 302, concierge
services 304, route obstruction information 306, a live human tour
guide 308, vehicle operational control information 310, and the
like.
[0040] For example, a vehicle operator or passenger may speak or
interact with the live human tour guide 308. The human tour guide
308 can have access to specific information about preferred routes,
points of interest, local culture, and the like. The human tour
guide 308 can assist in selecting a best route and may also provide
route impedance information for routes that are not preferred or
are currently under heavy traffic, under construction, or the
like.
[0041] As mentioned above, the vehicle operation intelligence
information 130 can be generated based on the gathered situational
awareness information 125. The one or more vehicles 115 and/or
occupants thereof can use the vehicle operational intelligence
information 130. For example, the one or more vehicles 115 can
automatically, efficiently, and safely navigate from one location
to another using the vehicle operational intelligence information
130.
[0042] FIG. 4 illustrates a diagram 400 of distributed intelligent
land buoys 105 connected to one or more remote servers 120 in
accordance with embodiments of the present invention. The one or
more remote servers 120 can include, for example, a workstation
410, a computer server 415, a database or storage farm 420, or the
like. The one or more remote servers 120 may be communicatively
coupled to one or more cellular towers 425 via an IP network 405.
The IP network 405 may be public or private. The one or more
cellular towers 425 may be communicatively coupled to the
distributed intelligent land buoys 105 via one or more cellular
networks 430. In addition or alternatively, the one or more
cellular towers 425 may be communicatively coupled to the
distributed intelligent land buoys 105 via one or more IP networks
including satellite networks, microwave radio networks, wired
networks, fiber optic networks, and the like.
[0043] FIG. 5 illustrates a diagram 500 of interconnected
intelligent land buoys 105 in accordance with embodiments of the
present invention. The interconnected intelligent land buoys 105
may be configured as a mesh or ad-hoc network. In this embodiment,
the operations and processing that would otherwise be performed by
the one or more remote servers 120 are instead performed by each of
the intelligent land buoys 105 of the network. In other words, in
this embodiment, there is no need for the remote servers 120.
Moreover, the data that is developed and processed by each of the
intelligent land buoys 105 can be automatically shared among the
other intelligent land buoys 105. In such manner, the situational
awareness information 125 can be obtained and processed by the
network itself. In addition, the network of interconnected
intelligent land buoys 105 can generate the operational
intelligence information 130 based on the gathered situational
awareness information 125.
[0044] FIG. 6 illustrates a diagram of an example system 600
including an intelligent land buoy 105 interconnected with a
vehicle 115, sensors 605 and 610, remote servers 615, third party
servers 620, and third party clients 625, in accordance with
embodiments of the present invention.
[0045] The intelligent land buoy 105 can include a processor 630 to
compress the situational awareness information 125, and storage 635
to store the situational awareness information 125 in either
compressed or uncompressed form. The intelligent land buoy 105 can
further include one or more short range radio transceivers 165,
each of which may have associated therewith an antenna. For
example, the short range radio transceivers can include a WIFI
compatible transceiver 640, a ZIGBEE compatible transceiver 645, a
BLUETOOTH compatible transceiver 650, and/or a HART compatible
transceiver 655. The intelligent land buoy 105 can further include
one or more long range radio transceivers 175, each of which may
have associated therewith an antenna. For example, the long range
radio transceivers can include a cellular compatible transceiver
660, a satellite compatible transceiver 665, a global positioning
system (GPS) compatible transceiver 670, or other suitable
transceiver 675. The short and/or long range transceivers can be
associated with a local area network (LAN) and/or a wide area
network (WAN). The cellular transceiver can be associated with Long
Term Evolution (LTE), Enhanced Voice-Data Optimized (EVDO), High
Speed Packet Access (HSPA), or other suitable cellular
technologies.
[0046] The intelligent land buoy 105 can include or otherwise be
communicatively connected with a visible light camera 170 to gather
the visible light camera information 210 (of FIG. 2), an infrared
camera 680 to gather the infrared camera information 212 (of FIG.
2), and/or a motion detector device 685 to gather the motion
detector information 214 (of FIG. 2). The intelligent land buoy 105
can further include a radar device 682 and/or a LIDAR device 684.
The intelligent land buoy 105 can further include a radio frequency
ID (RFID) tag 686, which can be passive or active. The tag can be
energized, for example, responsive to vehicles passing over an
inductive loop in the road.
[0047] The intelligent land buoy 105 can be communicatively coupled
to sensors 605 and/or 610. The sensors 605 and/or 610 may be
embedded in the road 110 or otherwise located adjacent to or nearby
the road 110. The intelligent land buoy 105 can receive situational
awareness information 125 from the sensor(s) via a wireless or
wired connection. The sensor 605 can be a temperature sensor and
the sensor 610 can be an inductive traffic sensor. The inductive
traffic sensor 610 can detect when a vehicle passes over and can
transmit a signal to the intelligent land buoy 105 representing
such an occurrence. The inductive traffic sensor 610 can also
transmit, to the intelligent land buoy 105, a count of the number
of times a vehicle passes over for a particular period of time.
[0048] The one or more remote servers 615 can receive and/or
transmit information via an application specific interface (API)
695. The one or more remote servers 615 can periodically receive
new situational awareness information 125 from each of the
intelligent land buoys 105 via the API 695. The one or more remote
computer servers 615 can receive external data 690 from one or more
devices not connected with the plurality of intelligent land buoys
105 via the API 695. The one or more remote computer servers 615
can receive client preference information from third party clients
625 via the API 695. The client preference information can include
a fuel economy preference (i.e., most fuel efficient route
preference), a transit time preference (i.e., a most efficient
route in terms of time), a proximity to points of interest
preference (i.e., the most scenic or interesting mute), an
avoidance of hazards preference (i.e., a safest route), or the
like. The one or more remote servers 615 can generate the vehicle
operational intelligence information 130 based at least on the
client preference information.
[0049] The one or more remote servers 615 can transmit and/or
receive the situational awareness information 125, the vehicle
operational intelligence information 130, and/or the client
preference information to a remote device such as the third party
servers 620. The third party clients 625 can subscribe to or
otherwise receive the operational intelligence information 130 from
the one or more remote servers 120.
[0050] The operational intelligence information 130 can be
monetized. For example, the third party clients 625 may pay value
for the operational intelligence information 130. The value can be
in the form of real currency such as dollars, credits, points,
digital or virtual currency, or the like. The value can be paid on
a per-time-unit basis, a per-data-unit basis, and/or as a one-time
access fee, or the like. The value can be automatically transferred
from the vehicle or third party client to the provider of the
operational intelligence information 130. Multiple tiers of
operational intelligence information 130 can be provided based on
corresponding tiers of value paid. For example, a safety tier can
provide navigation safety operational intelligence information. By
way of another example, a scenic tier can provide scenic or points
of interest operational intelligence information. By way of yet
another example, a fuel efficiency tier can provide fuel efficiency
operational intelligence information, and so forth. The vehicle
and/or third party client may opt-in or opt-out of the paid
service. As an alternative to a pay service, a free service may
provided in which the operational intelligence may include one or
more advertisements. The advertisements can be targeted. For
example, if the vehicle is in a particular town close to a
particular hotel, an advertisement for the particular hotel can be
provided to the vehicle or vehicle occupants.
[0051] The type of operational intelligence provided can be based
on the type of vehicle or third party client. For example, the
vehicle or third party client can broadcast an ID, an SSID, or the
like. Such ID or SSID may include the type of vehicle or client.
For example, some vehicles might be passenger automobiles, some
might be long-haul trucks, some might be pick-up trucks, some might
be electric vehicles, some might be sports cars, some might be
trains, and so forth. The operational intelligence can then be
customized to that particular type of vehicle. For example,
long-haul trucks cannot travel on all roads, but must be carefully
directed to navigate away from steep roads, narrow roads, tight
intersections, and the like. A sports car may prefer routes having
no speed bumps. Large vehicles may prefer fuel efficient routes.
Trains may prefer customized guidance for selecting routes based on
number of rail crossings, schedule conflicts, or the like. Such
preferences can be automatically determined based on the
broadcasted vehicle type, and incorporated within the operational
intelligence provided to the vehicle.
[0052] The intelligent land buoy 105 may receive situational
awareness information 125 directly from an antenna 698 located on
the vehicle 115. In addition or alternatively, the remote servers
615 may receive situational awareness information 125 directly from
the antenna 698 located on the vehicle 115.
[0053] FIG. 7 illustrates a diagram of various example third party
clients 625 in accordance with embodiments of the present
invention. The third party clients 625 can include, for example, a
smart phone 705, a map service 710, a computer 715, a GPS device
and/or navigator system 720, a tablet 725, a vehicle 730, a camera
735, a phone 740, or the like.
[0054] FIG. 8 illustrates a diagram 800 of an intelligent land buoy
105 associated with a route segment 805 of a road 110 having an
obstruction 810 impeding the flow of traffic. The intelligent land
buoy 105 can include identification logic 815 to identify, based at
least on the situational awareness information associated with the
route segment 805, the presence of the traffic obstruction 810. The
term "logic" as used herein can include software, hardware,
firmware, or any combination thereof. The intelligent land buoy 105
can include tracking logic 820 to track, based at least on the
situational awareness information associated with the route segment
805, the presence, position, and speed of the traffic obstruction
810. The traffic obstruction 810 can include a vehicle, a
pedestrian, an animal, road debris, an object, or the like. The
traffic obstruction can be an ambulance, police vehicle, or other
public service vehicle, and the one or more vehicles 115 can be
instructed to and/or autonomously controlled to automatically pull
to the side of the road or take an alternate route.
[0055] The intelligent land buoys 105 essentially have "machine
vision" in which objects can be recognized, identified, and
quantified. The intelligent land buoys 105 can also detect and
quantify vehicle traffic flow, speed, and/or density. For example,
the intelligent land buoys 105 can use machine vision to calculate
the current load and/or utilization efficiency of a route segment
(e.g., 805). Such calculation can include the number of vehicles on
the road, the capacity of the road, and the like.
[0056] While the vehicle 825 may have a direct line of sight to the
obstruction 810, the vehicles further back (e.g., 830 and 835) have
no direct line of sight to the obstruction 810, and therefore, such
vehicles conventionally are unable to take evasive action or plan
for alternative routes. Using the embodiments of the invention
described herein, the rearward vehicles (e.g., 830 and 835) can
receive operational intelligence information 130 from the
intelligent land buoy 105 so that alternate routes can be taken or
other evasive action can be taken before it is too late. Therefore,
the vehicles can travel from one point to another more safely and
efficiently. The operational intelligence 130 can be based at least
in part on the identification logic 815 and the tracking logic
820.
[0057] FIG. 9 illustrates a diagram of various kinds of alerts 905
in accordance with embodiments of the present invention. The alerts
905 can be provided to the one or more vehicles 115 (of FIG. 1),
the third party clients 625 (of FIG. 7), the third party servers
(of FIG. 6), and the like. In some embodiments, one or more of the
alerts 905 may be in a location proximate to an occupant of a
vehicle 115 to alert the occupant. The alerts 905 can include an
alert display 910 that is visible to the occupant, an audible alert
915, and/or a blinking light or other suitable light emission alert
920. The alerts 905 can be a tactile type alert. The alerts 905 may
be presented via the use of one or more monitors, portable devices
(such as a tablet or phone), a heads-up display, an audible prompt,
a vibration of one or more seats within a vehicle, or the like. The
alert may be a colored overlay on the display.
[0058] The vehicle operational intelligence 130 can include a
predictive awareness alert manifested through one or more of the
alerts 905 indicating a time period within which an occupant in a
particular vehicle from among the one or more vehicles 115 is
advised to take operational control of the particular vehicle. The
occupant can be prompted by one or more of the alerts 905 to either
take manual control of the vehicle or to select a new destination.
A threshold of acceptable risk can be defined based at least on
current legal regulations on a federal, state, or local level. The
enabling of the alert can be based on such threshold. The alert can
warn the operator or passenger of the vehicle of an impending or
approaching danger, such as a busy intersection, a rail crossing, a
sharp turn or curve, an accident, an obstacle, a pot hole, or the
like.
[0059] In the case where the occupant of the vehicle fails to act
(i.e., fails to take manual control of the vehicle) within a
predefined period of time, then the vehicle may autonomously take
action to ensure occupant safety by either slowing the vehicle,
choosing a new destination or route, and/or coming to a complete
stop at a safe location pending further action by the occupant.
[0060] FIG. 10A illustrates a diagram 1000 of an intelligent land
buoys 105 associated with multiple route segments (e.g., 1005,
1010, 1015, 1020, 1025, and 1030) in accordance with embodiments of
the present invention. The term "route segment" can refer to a
section of road, a highway, an intersection, or a point at which
multiple roads or highway segments intersect.
[0061] The intelligent land buoys 105 can gather the situational
awareness information about each of the route segments 1005 within
their domain. In other words, each of the intelligent land buoys
105 may have associated therewith one or more route segments 1005.
The route segments 1005 need not be exclusively associated with a
single intelligent land buoy 105, but rather in some cases, the
route segments 1005 may be associated with multiple intelligent
land buoys 105. The intelligent land buoys 105 can compress the
situational awareness information 125 about each of the route
segments 1005. The situational awareness information 125 can
include information from sensors 1080, or other situation awareness
information 125 described in detail herein.
[0062] In some embodiments, the intelligent land buoys 105 can
compress the situational awareness information 125 into a
corresponding route segment impedance score (e.g., score A, score
B, and score C) for each route segment (e.g., 1005, 1010, and
1015). In other words, multiple impedance scores can be generated
and stored in the intelligent land buoy 105. Each impedance score
can be associated with a corresponding route segment.
[0063] The situational awareness information 125 can be weighted
and/or normalized to facilitate their representation as numerical
impedance scores. The impedance scores can represent a range of
numeric values indicating the burden or desirability of a
particular segment of road or points along a route. The impedance
scores (e.g., score A, score B, and score C) can be stored by the
intelligent land buoy 105 and/or transmitted to the one or more
remote servers 120.
[0064] In some embodiments, the intelligent land buoy 105 can
aggregate the impedance scores into aggregated route segment
impedance scores 1045. The intelligent land buoys 105 can transmit
the aggregated route segment impedance scores 1045 to the one or
more remote computer servers 120.
[0065] In some embodiments, the intelligent land buoy 105 can
transmit the raw impedance scores (e.g., score A, score B, and
score C) for each corresponding route segment to the one or more
remote servers 120. The one or more remote servers 120 can
aggregate a first portion (e.g., including score A and score B) of
the route segment impedance scores associated with a first complete
route (e.g., including route segments 1005 and 1010) between a
first geographic location 1035 and a second geographic location
1040.
[0066] The one or more remote computer servers 120 can aggregate a
second portion (e.g., including score A, score B, and score C) of
the route segment impedance scores associated with a second
complete route (e.g., including route segments 1005, 1010, and
1015) between the first geographic location 1035 and the second
geographic location 1040. In similar fashion, the one or more
remote computer servers 120 can aggregate N portions of the route
segment impedance scores associated with corresponding N routes
between the first geographic location 1035 and the second
geographic location 1040. For example, N may be equal to or greater
than 3.
[0067] Put differently, the aggregated N portions of route segment
impedance scores 1045 may include the first aggregated portion of
scores, the second aggregated portion of scores, and so on, through
the Nth aggregated portion of scores. Each aggregated portion of
scores (e.g., aggregated portions 1.sup.st through Nth) represents
an aggregated impedance score of a complete route from point 1035
to point 1040.
[0068] FIG. 10B illustrates a table 1050 including example
associations between route segments (e.g., 1005, 1010, and 1015)
and corresponding impedance scores 1068 in accordance with
embodiments of the present invention. In this example, route
segment 1005 is determined to have an impedance score of 0.6, route
segment 1010 is determined to have an impedance score of -0.3, and
route segment 1015 is determined to have an impedance score of 0.8.
Each impedance score 1068 is a representation of the difficulty a
vehicle would have in traveling a particular route segment. The
difficultly can be influenced by various kinds of factors, which
are gathered as the situational awareness information described
above, and processed and compressed by the intelligent land buoys
105. Each factor can be assigned a different weight. The
intelligent land buoys 105 can generate the impedance scores
1068.
[0069] For example, the intelligent land buoys 105 can generate the
route segment impedance scores based at least on a plurality of
weighted factors, which can include rain, fog, visibility, traffic
density, pedestrian density, road hazard, road condition, rail
crossings, bridges, intersection points, or the like. In some
embodiments, the impedance score 1068 can range between positive
one (1) and negative one (-1), with a positive or higher relative
score representing greater difficultly, and a negative or lower
relative score representing less difficulty. It will be understood
that any suitable score scale can be used.
[0070] FIG. 10C illustrates a table 1055 including example
associations between complete mutes and aggregated portions of
route segment impedance scores in accordance with embodiments of
the present invention. In this example, the first complete route
includes route segments 1005 and 1010 (of FIG. 10A). A
corresponding aggregated portion of route segment scores for route
segments 1005 and 1010 is equal to 0.3 (i.e., 0.6+(-0.3)).
Similarly, the second complete route includes route segments 1005,
1010, and 1015 (of FIG. 10A). A corresponding aggregated portion of
route segment scores for route segments 1005, 1010, and 1015 is
equal to 1.1 (i.e., 0.6+(-0.3)+0.8). Similar aggregation of
portions of route segment scores can occur for N complete
routes.
[0071] In some embodiments, each aggregated portion can be
normalized (e.g., 1047), i.e., divided by the number of route
segments associated with that portion. For example, for the first
route, the aggregated value 0.3 can be divided by 2 (i.e., the
number of route segments for the first route), which results in a
normalized aggregated value 1062 of 0.15. By way of another
example, for the second mute, the aggregated value 1.1 can be
divided by 3 (i.e., the number of route segments for the second
route), which results in a normalized aggregated value of 0.367.
Such normalization can be applied to the aggregated portion of
route segments scores associated with the first route through the
Nth route.
[0072] The intelligent land buoys 105 (of FIG. 1) can transmit the
raw impedance scores and/or the aggregated N portions of route
segment impedance scores 1045 to the one or more remote computer
servers 120 (of FIG. 1). The one or more computer servers 120 can
receive such raw scores and/or aggregated portions from multiple
different intelligent land buoys 105. The one or more remote
computer servers 120 can select from among the aggregated N
portions 1045 a present lowest aggregated impedance score 1060
associated with a particular one complete route (e.g., first route)
from among the N complete routes. Such a selection can be made from
among the N complete routes associate with one or more intelligent
land buoys 105. The one or more remote computer servers 120 can
transmit a present lowest impedance route selection (e.g., first
route) to the plurality of intelligent land buoys 105 based at
least on the present lowest aggregated impedance score 1060.
[0073] In addition, the one or more remote computer servers 120 can
select from among the aggregated N portions a predicted future
lowest aggregated impedance score 1070 associated with a particular
complete route (e.g., first route) from among the N routes.
Similarly, the predicted future score 1075 can be based at least on
a history 1065 of prior impedance scores. For example, based on the
predictable and historical rush hour traffic, future congestion can
be predicted to occur during similar time periods. The one or more
remote computer servers 120 can determine a predicted lowest
impedance route selection (e.g., first route) based at least on the
predicted future lowest aggregated impedance scores 1070, 1075,
etc. The one or more remote computer servers 120 can transmit the
selection to the intelligent land buoys 105.
[0074] The one or more remote servers 120 can alter in real-time
the predicted lowest impedance route selection based at least on
the new situational awareness information 125 periodically
received. The one or more remote servers 120 can transmit the
altered predicted lowest impedance route selection to the
intelligent land buoys 105 and/or directly to the one or more
vehicles 115. The intelligent land buoys 105 may relay the altered
predicted lowest impedance route selection to the one or more
vehicles 115. In this manner, the one or more vehicles 115 can be
substantially up-to-date at all times with the route having the
predicted lowest impedance. As the real world situation changes
over time, so too can the vehicle automatically adjust and take the
route having the predicted lowest impedance. The vehicle can
autonomously re-route, slow down, speed up, stop, change direction,
or the like. The one or more vehicles 115 can receive the route
selection more frequently from intelligent land buoys 105 that are
relatively closer, and less frequently from intelligent land buoys
105 that are relatively farther away.
[0075] As mentioned above, the one or more remote servers 120 can
receive external data 690 (of FIG. 6) from one or more devices not
necessarily connected with the intelligent land buoys 105. The one
or more remote servers 120 can generate the predicted lowest
impedance route selection based at least on the external data.
[0076] FIG. 11 illustrates a diagram of an impedance function 1105
in the time domain in accordance with embodiments of the present
invention. The intelligent land buoys 105 can include impedance
score logic to generate impedance score probability functions
(e.g., 1105) in the time domain based on the route segment
impedance scores (e.g., 1068) for predefined time periods (e.g.,
1110). In other words, the time domain representation of impedance
for a given route segment can be described as a probability density
function.
[0077] The impedance score probability density function 1105 can be
representing as a set of values between -1 and 1 plotted on the
Y-Axis of a graph, and where time is plotted on the X-Axis. X=0 can
represent the current time. The X-Axis can be understood to cover a
variable range of time values such that a prediction or probability
of impedance scores 1068 may cover a span or hours, days, and/or
weeks.
[0078] The intelligent land buoys 105 can generate impedance score
probability functions such as 1105 in the time domain based on the
route segment impedance scores 1068 for predefined time periods.
The intelligent land buoys 105 can transform the impedance score
probability functions into the frequency domain. The frequency
domain transformation can be based at least on a Laplace series
transform. Fourier series transform, Z-Transform, or the like.
[0079] The intelligent land buoys 105 can transmit the transformed
impedance score probability functions to the one or more remote
computer servers 120. The one or more remote computer servers 120
can generate the predicted lowest impedance route selection (e.g.,
first route) based at least on the transformed impedance score
probability functions. In this manner, the impedance score
information is compressed and transmitted more efficiently. Rather
than transmit vast amounts of data over networks, which can
sometime suffer from slowness or latency, the large quantities of
situational awareness information 125 can instead be condensed into
a single transformed impedance score probability function for each
route segment. This technique allows for situational awareness
information 125 to be gathered and processed in real-time or near
real-time. Moreover, such technique enables the vehicle operational
intelligence 130 to be provided in real-time or near-real time. In
this manner, autonomous and semi-autonomous vehicles can have
access to real-time and predictive operational intelligence.
[0080] Other factors may influence the impedance score probability
functions. For example, the number of third party clients 625 (of
FIG. 6) and/or the number of vehicles 115 can affect the impedance
score probability function. A relatively higher number of vehicles
requesting information for a particular road segment or point may
indicate a predictive factor of future congestion. The one or more
remote servers 120 may also receive inputs or informational "feeds"
using APIs from third parties (such as third party servers 620
and/or third party clients 625 of FIG. 6). These "feeds" or "input
feeds" can provide additional inputs such as points of interest
(e.g., locations of wineries, vistas, historical sites, etc.),
proximity and utilization of electric vehicle charging stations,
timing and location of events such as marathons, large gatherings
of people, and the like. Such factors may influence the impedance
score probability functions.
[0081] The one or more remote servers 120 may also transmit outputs
or information "feeds" using the APIs from third parties (such as
third party servers 620 and/or third party clients 625 of FIG. 6).
These "feeds" or "output feeds" can include the operational
intelligence information 130. More specifically, such "output
feeds" can include the route impedance information described in
detail above. The third party clients, such as a map service, a
navigation device, and the like, can receive and incorporate the
route impedance information into its functionality to provide a
more accurate service.
[0082] FIG. 12 is a flow diagram 1200 illustrating a technique for
providing dynamic routing intelligent vehicle enhancement services
in accordance with embodiments of the present invention. The
technique begins at 1205 where intelligent land buoys can gather
situational awareness information about roadways and one or more
vehicles traveling thereon. The flow proceeds to 1210 where the
situational awareness information is compressed, as discussed in
detail above. At 1215, the compressed situational awareness
information can be transmitted by the intelligent land buoys to one
or more remote servers. At 1220, the intelligent land buoys can
receive, from the one or more remote servers, vehicle operational
intelligence information, as also described in detail above. The
flow then proceeds to 1225, where the intelligent land buoys can
transmit the vehicle operational intelligence information to one or
more autonomous or semi-autonomous vehicles.
[0083] FIG. 13 is a flow diagram 1300 illustrating another
technique for providing dynamic routing intelligent vehicle
enhancement services in accordance with embodiments of the present
invention. The technique begins at 1305 where one or more remote
servers receive, from one or more intelligent land buoys,
compressed situational awareness information about roadways and one
or more vehicles traveling thereon. At 1310, the one or more remote
servers can de-compress the situational awareness information. The
flow proceeds to 1315 where the one or more remote computer servers
can process the de-compressed situational awareness information. At
1320, the one or more remote servers can receive external data from
one or more devices not connected to the intelligent land
buoys.
[0084] The flow then proceeds to 1325, where the one or more remote
computer servers can generate vehicle operational intelligence
information based at least on the de-compressed situational
awareness information and the external data. The operational
intelligence information can be transmitted by the one or more
remote servers in one or more fashion. For example, the flow can
proceed to 1330, where the one or more remote servers transmit the
operational intelligence information to the intelligent land buoys.
In addition or alternatively, the one or more remote servers can
directly transmit at 1335 the operational intelligence information
to one or more autonomous or semi-autonomous vehicles. In addition
or alternatively, at 1340, the one or more remote servers can
transmit the operational intelligence information to third party
clients.
[0085] The inventive aspects disclosed herein are particularly
advantageous because they increase safety, reduce congestion,
provide environmental benefits, reduce the fuel consumption, and
provide avoidance of routes under construction. A smart grid is
provided, which improves the utility and capacity of existing
roads, and enables autonomous and semi-autonomous vehicles to more
safely and effectively navigate such roads. The intelligent land
buoys are akin to a utility service, which can receive value and
exchange value for scarce and highly useful operational
intelligence information.
[0086] In other embodiments, the intelligent land buoys can detect
if a vehicle is not performing properly or not within government
clean air or other requirements, and if so, such information can be
transferred to a government body, or the vehicle might be
instructed to obtain the appropriate mechanical service to remedy
the deficiency. Advanced operational intelligence is provided to
the vehicle and/or vehicle operator. In addition, preemptive alerts
and notifications may be transmitted to the vehicle, a computer
within the vehicle, an occupant, and/or an operator. Neural
networks or other machine learning mechanisms can be used. For
example, the intelligent land buoys and/or the remote computer
servers can be part of a neural network.
[0087] The following discussion is intended to provide a brief,
general description of a suitable machine or machines in which
certain aspects of the invention can be implemented. Typically, the
machine or machines include a system bus to which is attached
processors, memory. e.g., random access memory (RAM), read-only
memory (ROM), or other state preserving medium, storage devices, a
video interface, and input/output interface ports. The machine or
machines can be controlled, at least in part, by input from
conventional input devices, such as keyboards, mice, etc., as well
as by directives received from another machine, interaction with a
virtual reality (VR) environment, biometric feedback, or other
input signal. As used herein, the term "machine" is intended to
broadly encompass a single machine, a virtual machine, or a system
of communicatively coupled machines, virtual machines, or devices
operating together. Exemplary machines include computing devices
such as personal computers, workstations, servers, portable
computers, handheld devices, telephones, tablets, etc., as well as
transportation devices, such as private or public transportation,
e.g., automobiles, trains, cabs, etc.
[0088] The machine or machines can include embedded controllers,
such as programmable or non-programmable logic devices or arrays,
Application Specific Integrated Circuits (ASICs), embedded
computers, smart cards, and the like. The machine or machines can
utilize one or more connections to one or more remote machines,
such as through a network interface, modem, or other communicative
coupling. Machines can be interconnected by way of a physical
and/or logical network, such as an intranet, the Internet, local
area networks, wide area networks, etc. One skilled in the art will
appreciate that network communication can utilize various wired
and/or wireless short range or long range carriers and protocols,
including radio frequency (RF), satellite, microwave, Institute of
Electrical and Electronics Engineers (IEEE) 545.11, Bluetooth.RTM.,
optical, infrared, cable, laser, etc.
[0089] Embodiments of the invention can be described by reference
to or in conjunction with associated data including functions,
procedures, data structures, application programs, etc. which when
accessed by a machine results in the machine performing tasks or
defining abstract data types or low-level hardware contexts.
Associated data can be stored in, for example, the volatile and/or
non-volatile memory, e.g., RAM, ROM, etc., or in other storage
devices and their associated storage media, including hard-drives,
floppy-disks, optical storage, tapes, flash memory, memory sticks,
digital video disks, biological storage, etc. Associated data can
be delivered over transmission environments, including the physical
and/or logical network, in the form of packets, serial data,
parallel data, propagated signals, etc., and can be used in a
compressed or encrypted format. Associated data can be used in a
distributed environment, and stored locally and/or remotely for
machine access.
[0090] Having described and illustrated the principles of the
invention with reference to illustrated embodiments, it will be
recognized that the illustrated embodiments can be modified in
arrangement and detail without departing from such principles, and
can be combined in any desired manner. And although the foregoing
discussion has focused on particular embodiments, other
configurations are contemplated. In particular, even though
expressions such as "according to an embodiment of the invention"
or the like are used herein, these phrases are meant to generally
reference embodiment possibilities, and are not intended to limit
the invention to particular embodiment configurations. As used
herein, these terms can reference the same or different embodiments
that are combinable into other embodiments.
[0091] Embodiments of the invention may include a non-transitory
machine-readable medium comprising instructions executable by one
or more processors, the instructions comprising instructions to
perform the elements of the inventive concepts as described
herein.
[0092] Consequently, in view of the wide variety of permutations to
the embodiments described herein, this detailed description and
accompanying material is intended to be illustrative only, and
should not be taken as limiting the scope of the invention. What is
claimed as the invention, therefore, is all such modifications as
may come within the scope and spirit of the following claims and
equivalents thereto.
* * * * *