U.S. patent application number 17/688386 was filed with the patent office on 2022-06-16 for safety and performance integration device for non-autonomous vehicles.
The applicant listed for this patent is Autoligence Inc.. Invention is credited to William Collins White, III.
Application Number | 20220187098 17/688386 |
Document ID | / |
Family ID | 1000006181308 |
Filed Date | 2022-06-16 |
United States Patent
Application |
20220187098 |
Kind Code |
A1 |
White, III; William
Collins |
June 16, 2022 |
SAFETY AND PERFORMANCE INTEGRATION DEVICE FOR NON-AUTONOMOUS
VEHICLES
Abstract
A method, apparatus, and system for integrating a non-autonomous
vehicle into a transit environment populated with autonomous
vehicles. An autonomous vehicle network integration apparatus is
disclosed which collects data over a wireless network regarding a
vehicle's route and surroundings, including nearby fully and
partially autonomous vehicles. The apparatus is configured to
analyze data and dynamically determine a range of influence, within
which it communicates with vehicles to suggest driver actions and
inform self-driving vehicle behavior.
Inventors: |
White, III; William Collins;
(Palm Coast, FL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Autoligence Inc. |
St. Augustine |
FL |
US |
|
|
Family ID: |
1000006181308 |
Appl. No.: |
17/688386 |
Filed: |
March 7, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16670792 |
Oct 31, 2019 |
11268825 |
|
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17688386 |
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62879101 |
Jul 26, 2019 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G07C 5/008 20130101;
G01C 21/3691 20130101; G07C 5/0808 20130101; G01C 21/3492
20130101 |
International
Class: |
G01C 21/36 20060101
G01C021/36; G01C 21/34 20060101 G01C021/34; G07C 5/08 20060101
G07C005/08; G07C 5/00 20060101 G07C005/00 |
Claims
1. A method for integrating a non-autonomous vehicle into an
autonomous vehicle network, comprising: activating an autonomous
vehicle network integration application on a mobile device detected
within a primary vehicle; detecting a destination from the mobile
device; detecting a route to the destination from the mobile
device; detecting environmental conditions along the route based on
vehicle sensors on the primary vehicle and data accessed through
the mobile device; detecting traffic conditions along the route
based on data accessed through the mobile device; calculating a
base influence range using the environmental conditions and the
traffic conditions; monitoring on-board diagnostic data during
operation of the primary vehicle, wherein the on-board diagnostic
data includes location, velocity, and direction; calculating an
influence vector based on the location, the velocity, and the
direction; dynamically updating a moving influence range based on
the base influence range and the influence vector; detecting at
least one secondary vehicle within at least one of the base
influence range and the moving influence range; transmitting the
influence vector to the secondary vehicle; receiving a secondary
influence vector from the secondary vehicle; dynamically updating
the moving influence range based on the secondary influence vector;
and providing continuous guidance to a driver through the mobile
device, wherein the continuous guidance comprises route navigation
guidance and hazard avoidance guidance.
2. The method of claim 1, further comprising: detecting the driver
within the primary vehicle; loading a driver profile for the driver
detected; and modifying at least one of the base influence range
and the influence vector based on the driver profile.
3. The method of claim 2, wherein the driver is detected by
selecting the driver from the autonomous vehicle network
integration application.
4. The method of claim 2, wherein the driver is detected by
detecting the mobile device associated with the driver.
5. The method of claim 2, further comprising: logging driver
actions, wherein the driver actions include acceleration,
deceleration, turns, lane excursions, braking, and signaling;
detecting when the driver actions indicate hazardous driving; and
updating the driver profile when the driver actions indicating the
hazardous driving are detected.
6. The method of claim 2, wherein the driver profile includes a
priority metric.
7. The method of claim 1 further comprising: loading a vehicle
profile for the primary vehicle, wherein the vehicle profile
comprises at least one of mass, engine power, acceleration
capability, deceleration capability, turning radius, the vehicle
sensors available, an automation level, and other physical and
performance parameters; and modifying at least one of the base
influence range and the influence vector based on the vehicle
profile.
8. The method of claim 1, further comprising: calculating a base
awareness range, wherein the base awareness range extends beyond
the base influence range; dynamically calculating a moving
awareness range based on the base awareness range and the influence
vector; detecting the secondary vehicle within the moving awareness
range; and transmitting the influence vector to the secondary
vehicle within the moving awareness range.
9. The method of claim 1, further comprising: calculating a base
hazard range, wherein the base hazard range lies within the base
influence range; dynamically calculating a moving hazard range
based on the base hazard range and the influence vector; and
providing the hazard avoidance guidance when obstacles are detected
within the moving hazard range.
10. The method of claim 1, further comprising calculating the
secondary influence vector based on input from the vehicle sensors
on condition that the secondary influence vector is not received
from the secondary vehicle.
11. An autonomous vehicle network integration apparatus, the
autonomous vehicle network integration apparatus comprising: an
on-board diagnostics connection port; a wireless transceiver; a
processor; and a memory storing instructions that, when executed by
the processor, configure the apparatus to: activate an autonomous
vehicle network integration application on a mobile device detected
within a primary vehicle; detect a destination from the mobile
device; detect a route to the destination from the mobile device;
detect environmental conditions along the route based on vehicle
sensors on the primary vehicle and data accessed through the mobile
device; detect traffic conditions along the route based on data
accessed through the mobile device; calculate a base influence
range using the environmental conditions and the traffic
conditions; monitor on-board diagnostic data during operation of
the primary vehicle, wherein the on-board diagnostic data includes
location, velocity, and direction; calculate an influence vector
based on the location, the velocity, and the direction; dynamically
update a moving influence range based on the base influence range
and the influence vector; detect at least one secondary vehicle
within the moving influence range; transmit the influence vector to
the secondary vehicle within the moving influence range; receive a
secondary influence vector from the secondary vehicle; dynamically
update the moving influence range based on the secondary influence
vector; and provide continuous guidance to a driver, wherein the
continuous guidance comprises route navigation guidance and hazard
avoidance guidance.
12. The autonomous vehicle network integration apparatus of claim
11, wherein the on-board diagnostics connection port comprises a
wired connection between an on-board diagnostics system of the
primary vehicle.
13. The autonomous vehicle network integration apparatus of claim
11, wherein the on-board diagnostics connection port comprises an
on-board diagnostics wireless transceiver physically connected to
an on-board diagnostics system of the primary vehicle in
communication with the wireless transceiver of the autonomous
vehicle network integration apparatus.
14. The autonomous vehicle network integration apparatus of claim
11, further comprising a universal serial bus port.
15. An autonomous vehicle network integration system, the
autonomous vehicle network integration system comprising: a primary
vehicle, wherein the primary vehicle is not an autonomous vehicle
and the primary vehicle includes an on-board diagnostics system; a
mobile device, wherein the mobile device is configured with an
autonomous vehicle network integration application; and an
autonomous vehicle network integration apparatus, wherein the
autonomous vehicle network integration apparatus is configured to:
activate the autonomous vehicle network integration application on
the mobile device detected within the primary vehicle; detect a
destination from the mobile device; detect a route to the
destination from the mobile device; detect environmental conditions
along the route based on vehicle sensors on the primary vehicle and
data accessed through the mobile device; detect traffic conditions
along the route based on data accessed through the mobile device;
calculate a base influence range using the environmental conditions
and the traffic conditions; monitor on-board diagnostic data during
operation of the primary vehicle, wherein the on-board diagnostic
data includes location, velocity, and direction; calculate an
influence vector based on the location, the velocity, and the
direction; dynamically update a moving influence range based on the
base influence range and the influence vector; detect at least one
secondary vehicle within the moving influence range; transmit the
influence vector to the secondary vehicle within the moving
influence range; receive a secondary influence vector from the
secondary vehicle; dynamically update the moving influence range
based on the secondary influence vector; and provide continuous
guidance to a driver, wherein the continuous guidance comprises
route navigation guidance and hazard avoidance guidance.
16. The autonomous vehicle network integration system of claim 15,
wherein the primary vehicle is a low priority vehicle, the
secondary vehicle is a high priority vehicle, and the route
navigation guidance comprises proactive routing instructions
configured to guide the low priority vehicle to the destination
using the route that minimizes interference with the high priority
vehicle.
17. The autonomous vehicle network integration system of claim 15,
the autonomous vehicle network integration apparatus further
configured to: detecting the driver within the primary vehicle;
load a driver profile for the driver detected; and load a vehicle
profile for the primary vehicle, wherein the vehicle profile
comprises at least one of mass, engine power, acceleration
capability, deceleration capability, turning radius, the vehicle
sensors available, an automation level, and other physical and
performance parameters.
18. The autonomous vehicle network integration system of claim 16,
further comprising an autonomous vehicle network integration data
management center, wherein the autonomous vehicle network
integration data management center stores at least one of the
driver profile and the vehicle profile.
19. The autonomous vehicle network integration system of claim 17,
wherein the autonomous vehicle network integration data management
center interacts with third-party databases to collect data for
inclusion in at least one of the driver profile and the vehicle
profile.
20. The autonomous vehicle network integration system of claim 17,
wherein the autonomous vehicle network integration apparatus
communicates with the autonomous vehicle network integration data
management center through a dedicated communication channel.
21. The autonomous vehicle network integration system of claim 17,
wherein the autonomous vehicle network integration apparatus
communicates with the autonomous vehicle network integration data
management center through a mesh network, wherein the mesh network
comprises at least one additional wireless transceiver detected
within at least one of the base influence range and the moving
influence range.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application claims the benefit of priority under 35 USC
119(e) of U.S. non-provisional application Ser. No. 16/670,792,
filed on Oct. 31, 2019 and of U.S. provisional application No.
62/879,101, filed on Jul. 26, 2019.
BACKGROUND
[0002] As autonomous vehicles (e.g. self-driving or "smart
vehicles") populate the road in increasing numbers, their benefits
with regard to safety, emissions, and traffic reduction will become
increasingly clear. Fully autonomous vehicles are designed with a
sophisticated and redundant suite of sensors, such as RADAR, LIDAR,
ultrasound, and cameras that monitor the autonomous vehicle's
nearby and distant surroundings continually. They typically include
a wireless transceiver by which they may communicate with a global
positioning system (GPS), centralized fleet control, and each
other.
[0003] In spite of the advantages autonomous vehicles will provide,
it is likely that widespread adoption will be slow. The established
presence of "dumb" or non-autonomous vehicles on the roads, and
their track-record of over a century, may lead many consumers to
resist adopting such a new and comparatively untested technology.
In addition, the sophistication and complexity of the systems
needed to implement autonomous vehicle technology incurs high
costs. Self-driving cars may be economically out of reach for most
drivers for years to come.
[0004] An aftermarket solution is needed to integrate
non-autonomous vehicles into the network of autonomous vehicles.
Such a solution may improve consumer receptivity to increasing
automation levels, and may improve autonomous vehicle performance
by providing a mechanism by which autonomous vehicles may influence
and be influenced by more of the vehicles they encounter on the
roads.
BRIEF SUMMARY
[0005] This disclosure relates to autonomous vehicle network
integration (AVNI), a method for integrating a non-autonomous
vehicle into an autonomous vehicle network. This method comprises
activating an autonomous vehicle network integration application on
a mobile device detected within a primary vehicle. A destination
and a route to the destination are detected from the mobile device.
Environmental conditions along the route are detected using on
vehicle sensors on the primary vehicle and data accessed through
the mobile device. Traffic conditions are detected along the route
based on data accessed through the mobile device. A base influence
range is calculated using the environmental conditions and the
traffic conditions. On-board diagnostic data (OBD data) is
monitored during operation of the primary vehicle. OBD data
monitored includes location, velocity, and direction. An influence
vector is calculated based on the location, the velocity, and the
direction. A moving influence range is dynamically updated based on
the base influence range and the influence vector. At least one
secondary vehicle is detected within at least one of the base
influence range and the moving influence range. The influence
vector is transmitted to the secondary vehicle, and a secondary
influence vector is received from the secondary vehicle. The moving
influence range is dynamically updated based on the secondary
influence vector. Continuous guidance is provided to a driver
through the mobile device, wherein the continuous guidance
comprises route navigation guidance and hazard avoidance
guidance.
[0006] This disclosure further relates to autonomous vehicle
network integration apparatus comprising an on-board diagnostics
connection port, a wireless transceiver, a processor, and a memory
storing instructions that, when executed by the processor,
implement the method disclosed herein. Finally, this disclosure
relates to an autonomous vehicle network integration system
comprising a primary vehicle, wherein the primary vehicle is not an
autonomous vehicle and the primary vehicle includes an on-board
diagnostics system; a mobile device, wherein the mobile device is
configured with an autonomous vehicle network integration
application; and the autonomous vehicle network integration
apparatus disclosed herein.
[0007] While the autonomous vehicle network integration apparatus
and autonomous vehicle network integration system disclosed
comprise technology primarily expected in road transport vehicles,
the method of the present disclosure may be expanded to include
rail, marine, and air transportation. "Dumb trains," "dumb boats,"
and "dumb planes" may be configured with an analogous solution in
order to integrate with networks of autonomous rail engines,
watercraft, and aircraft, respectively.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0008] To easily identify the discussion of any particular element
or act, the most significant digit or digits in a reference number
refer to the figure number in which that element is first
introduced.
[0009] FIG. 1 illustrates a routine for integrating a
non-autonomous vehicle into an autonomous vehicle network, in
accordance with one embodiment.
[0010] FIG. 2 illustrates an autonomous vehicle network integration
system 200 in accordance with one embodiment.
[0011] FIG. 3 illustrates an autonomous vehicle network integration
apparatus 300 in accordance with one embodiment.
[0012] FIG. 4 illustrates an autonomous vehicle network integration
application user interface 400 in accordance with one
embodiment.
[0013] FIG. 5 illustrates an on-board diagnostics system 500 in
accordance with one embodiment.
[0014] FIG. 6 illustrates an influence vector and ranges 600 in
accordance with one embodiment.
[0015] FIG. 7 illustrates a base range levels 700 in accordance
with one embodiment.
[0016] FIG. 8 illustrates a moving range levels 800 in accordance
with one embodiment.
[0017] FIG. 9 illustrates an influence vector and range comparison
by velocity 900 in accordance with one embodiment.
[0018] FIG. 10 illustrates an influence vector and range comparison
by mass 1000 in accordance with one embodiment.
[0019] FIG. 11 illustrates routing related decisions 1100 in
accordance with one embodiment.
[0020] FIG. 12 illustrates a hazard and secondary influence
decisions 1200 in accordance with one embodiment.
[0021] FIG. 13 illustrates a dedicated communication channel 1300
in accordance with one embodiment.
[0022] FIG. 14 illustrates a mesh network 1400 in accordance with
one embodiment.
DETAILED DESCRIPTION
[0023] The method, apparatus, and system disclosed herein provide
an aftermarket solution for allowing a non-autonomous vehicle to
seamlessly communicate with and interact with autonomous vehicles
in their vicinity and, in some embodiments, an autonomous vehicle
network integration data management center. By means of a wireless
transceiver incorporated into an autonomous vehicle network
integration apparatus, information may be collected from the data
management center and a mobile device within the vehicle, as well
as autonomous or semi-autonomous vehicles around them.
[0024] In addition to collecting information from surrounding
vehicles and other sources, the autonomous vehicle network
integration apparatus disclosed herein may transmit similar data to
surrounding vehicles, allowing similarly equipped vehicles to
better anticipate and adjust for the non-autonomous vehicle
disclosed. Data collected may also be used to continually generate
guidance for the driver. This continuous guidance may take the form
of route navigation guidance and/or hazard avoidance guidance. This
guidance may be provided to the driver by means of audible or
visual alerts and notifications on the driver's mobile device.
[0025] FIG. 1 illustrates a routine 100 for integrating a
non-autonomous vehicle into an autonomous vehicle network, in
accordance with one embodiment. This routine 100 may be performed
by an autonomous vehicle network integration system 200 which
includes an autonomous vehicle network integration apparatus 300,
as illustrated in FIG. 2 and FIG. 3.
[0026] The routine 100 begins with a block 102, in which an
autonomous vehicle network integration application is activated on
a mobile device. This mobile device may be a cell phone, a tablet
computer, a GPS navigational display configured to implement the
tasks disclosed herein, or some other portable means of collecting
data over a wireless network and displaying information to a
driver. An embodiment of a user interface for this autonomous
vehicle network integration application is described in more detail
with regard to FIG. 4.
[0027] The routine 100 continues with a block 104, in which a
destination and a route to the destination are detected from the
mobile device. In block 106, environmental conditions and traffic
conditions are then detected along the route. The environmental
conditions and traffic conditions are used in block 108 to
calculate a base influence range. For example, if heavy rains are
detected along the route, the base influence range may be expanded
to compensate for a potential reduction in visibility and braking
performance. If heavy traffic is anticipated, the base influence
range may be reduced in order to limit the number of wireless
communications the primary vehicle may perform and the associated
power consumption.
[0028] During primary vehicle operation, at block 110, on-board
diagnostic data (OBD data) is monitored. This OBD data may be used
to determine the primary vehicle's velocity, while route
information gathered from the mobile device may be used to
determine the primary vehicle's location and direction. At block
112, the routine 100 uses the location, velocity, and direction to
calculate an influence vector. An illustration of the systems
providing OBD data, as well as the data provided that may be used
to implement the disclosed solution, is provided in FIG. 5.
[0029] Routine 100 continues with block 114, in which a moving
influence range is dynamically updated based on the base influence
range and the influence vector. The relationship between base
influence range, influence vector, and moving influence range in
accordance with one embodiment is illustrated in FIG. 6. In some
embodiments, a base awareness range and a moving awareness range
may be established which extend beyond the corresponding influence
ranges. These awareness ranges may be used to provide additional
reaction time by earlier transmission and reception of influence
vectors in specific circumstances. A base hazard range and a moving
hazard range may be established within the corresponding influence
ranges in some embodiments to provide a mechanism to trigger
critical alerts when conditions change or hazards are detected in
close proximity to the primary vehicle. These additional ranges are
illustrated in FIG. 7 and FIG. 8. Factors which may in some
embodiments affect the influence vector and thus the moving
influence range are illustrated in FIG. 9, and FIG. 10.
[0030] As the primary vehicle is operated, a secondary vehicle may
be detected within the base influence range or moving influence
range, in accordance with block 116. When this occurs, the
influence vector is transmitted to the secondary vehicle (block
118) and a secondary influence vector is received from the
secondary vehicle (block 120). The transmission and reception of
influence vectors may be a primary mechanism by which the
non-autonomous vehicle may be integrated into the autonomous
vehicle network. At block 122, routine 100 dynamically updates the
primary vehicle's moving influence range based on the secondary
influence vector received from the secondary vehicle. For example,
should some aspect of the secondary influence vector indicate that
the autonomous secondary vehicle has high priority, the moving
influence range for the primary vehicle may be reduced such that
the primary vehicle may cede right of way to the secondary
vehicle.
[0031] Finally, in block 124 of routine 100, the mobile device is
used to provide continuous guidance to the driver based on the data
collected and the vectors and ranges calculated. For example,
audible alerts may be generated when a secondary vehicle is
detected within the influence range. Weather conditions may be
reported. Driving instructions may be given which will reduce
travel time. Examples of continuous guidance are illustrated in
FIG. 11 and FIG. 12.
[0032] FIG. 2 illustrates an autonomous vehicle network integration
system 200 in accordance with one embodiment. The autonomous
vehicle network integration system 200 comprises a primary vehicle
202; a driver 204; a mobile device 206; an autonomous vehicle
network 208; a secondary vehicle(s) 210; an autonomous vehicle
network integration data management center 212; a third-party
databases 214; an autonomous vehicle network integration apparatus
300; and an on-board diagnostics system 500.
[0033] A driver 204 may enter their primary vehicle 202 carrying a
mobile device 206, which may trigger the beginning of routine 100.
The autonomous vehicle network integration apparatus 300 located in
the primary vehicle 202 may communicate with the mobile device 206
to begin collecting information. The autonomous vehicle network
integration application may be configured with a default driver
204, and that driver's driver profile 232 may be stored in memory
on the mobile device or within the autonomous vehicle network
integration apparatus 300. Alternately, the autonomous vehicle
network integration application may include a selection menu that
allows the driver 204 to self-identify through the autonomous
vehicle network integration application user interface 400 (see
FIG. 4 for an example user interface). The driver profile 232 may
also be stored in a database available through wireless
communication, and the autonomous vehicle network integration
apparatus 300 may be configured to access the driver profile from
an autonomous vehicle network integration data management center
212.
[0034] The driver profile 232 may be used to calculate the base
influence range. For example, an experienced driver with a clean
accident record may result in a smaller base influence range, as
their responses may be assumed to be quick and practiced, allowing
them to avoid hazards more rapidly, easily, and reliably. A
beginning driver, on the other hand, may result in a larger base
influence range, so that they receive notifications with more time
to respond, and more distant vehicles may receive transmissions,
allowing other semi-autonomous or fully autonomous vehicles to
maintain a larger buffer of distance from the primary vehicle.
[0035] In other embodiments, the driver profile 232 may include a
priority metric 236. This priority metric 236 may be based on
economic, civic, or safety factors, and may give the associated
driver priority over autonomous vehicles and/or traffic control
devices. The result may be similar to the way in which emergency
vehicle drivers may interact with traffic lights such that the
emergency vehicle may be given a green light as it approaches an
intersection. However, through the current disclosure, this
influence may be automatically signaled over a wireless network,
without need for a separate device requiring manual activation.
[0036] Some or all of the information in the driver profile 232
may, in some embodiments, be drawing from one or more third-party
databases 214. Such databases might include state transportation
registry databases, national registries, private insurance
databases, the Internal Revenue Service information repository, or
other public or private sources of information.
[0037] In some embodiments, the autonomous vehicle network
integration apparatus 300 may be configured to detect vehicle
startup without a recognized mobile device 206, and in such a case,
send an alert to a registered mobile device 206. The autonomous
vehicle network integration apparatus 300 may be configured to
audibly alert the driver 204 within the primary vehicle 202 that a
registered mobile device 206 has not been detected.
[0038] In some embodiments, the autonomous vehicle network
integration apparatus 300 may generate updates to the driver
profile 232 based on driver actions 224 detected through on-board
diagnostic data 216 (OBD data) collected from the on-board
diagnostics system 500. OBD data, gathered as described in further
detail with respect to FIG. 5, may include acceleration,
deceleration, turns, lane excursions, braking, and signaling. Turns
executed without a threshold level of deceleration or without
signaling may, for example, be logged as hazardous driving. A
velocity maintained in excess of a speed limit detected as part of
route data may similarly be logged as hazardous driving. A log may
be maintained of all driver actions 224, and some threshold
percentage of actions detected as hazardous driving may be used to
adjust the driver profile such that a larger base influence range
results.
[0039] Similar to the driver profile 232, the autonomous vehicle
network integration apparatus 300 may be configured to load a
vehicle profile 234 for the primary vehicle 202, wherein the
vehicle profile 234 comprises at least one of mass 238, engine
power 240, acceleration capability 242, deceleration capability
244, turning radius 246, vehicle sensors available 248, an
automation level 250, and other physical and performance parameters
252. The vehicle profile 234 may be stored in memory on the mobile
device 206 or on the autonomous vehicle network integration
apparatus 300, or may be available through a connection to the
autonomous vehicle network integration data management center
212.
[0040] Vehicle profile 234 information may be used to reduce or
expand the base influence range based on vehicle performance,
handling, and reliability. Vehicle mass 238 may be used in
calculating an influence vector 218, such that the magnitude of the
influence vector 218 for a larger vehicle may be greater than that
for a smaller vehicle, all other parameters being equal, as
illustrated in FIG. 10. Vehicles with vehicle sensors available 248
such as back-up cameras and proximity sensors may be allocated a
smaller base influence range, and information from these sensors
may be used to provide a higher level of automation.
[0041] Destination/location/route information 226 may be collected
through the interaction of an autonomous vehicle network
integration application with a third-party navigation application,
such as Google Maps.TM., Waze.TM., or Citymapper.TM.. In some
embodiments, the autonomous vehicle network integration application
may be capable of accepting destination entry and of calculating a
preferred route. Traffic conditions 228 along the route may be
detected through Google Maps, Waze, or other traffic tracking
applications. Changes in surrounding traffic conditions 228 may be
used to increase or decrease the base influence range in order to
prioritize safety, performance, or power consumption.
[0042] Third-party weather applications, such as Weather.com.TM.,
AccuWeather.TM., or Weather Bug.TM., may be used to detect rain,
fog, or other environmental conditions 230 along the route that
might pose a hazard as the primary vehicle 202 traverses the route.
Alternately, environmental conditions 230 may be available through
the autonomous vehicle network integration data management center
212 from third-party databases 214. Detection of hazardous weather
and other environmental conditions 230 along the route may result
in an expanded moving influence range or a larger influence vector.
Other environmental conditions 230 may include elevation changes.
For example, a steep downhill grade along a route may impact the
influence vector.
[0043] As the primary vehicle 202 traverses a route, the autonomous
vehicle network integration apparatus 300 may interact with
secondary vehicle(s) 210 as they enter the primary vehicle's
influence range. Secondary vehicle(s) 210 may be part of an
autonomous vehicle network 208, and so may include a mechanism for
wireless communication with each other and with the primary vehicle
202. When a secondary vehicle enters the base influence range or
moving influence range of the primary vehicle 202, the primary
vehicle 202 may transmit its influence vector 218, as indicated in
routine 100. The secondary vehicle(s) 210 may use this influence
vector to adjust their velocity, acceleration, or other behavior in
order to interact with the primary vehicle 202 safely, and in a
manner that has the least impact on autonomous vehicle
performance.
[0044] The primary vehicle 202 may in turn receive a secondary
influence vector 220 from autonomous secondary vehicle(s) 210 that
enter its range of influence. The autonomous vehicle network
integration apparatus 300 may use this secondary influence vector
220 to recalculate the base influence range and/or moving influence
range. The autonomous vehicle network integration apparatus 300 may
also use the secondary influence vector 220 to generate continuous
guidance 222 for the driver 204. Continuous guidance 222 may be
intended to instruct the driver 204 to avoid hazards (hazard
avoidance guidance), such as secondary vehicle(s) 210 that are not
slowing down as they approach and so may potentially collide with
the primary vehicle 202. Continuous guidance 222 may alternately be
route navigation guidance. Route navigation guidance may include
instructions to the driver 204 to slow down or speed up in order to
smoothly and quickly navigate around one or more secondary
vehicle(s) 210, instructions to get out of the way of a high
priority vehicle, or instructions to turn or otherwise adjust the
route to avoid secondary vehicle(s) 210.
[0045] In an embodiment, route navigation guidance may include
proactive routing instructions. These proactive routing
instructions help to manage traffic as opposed to just reacting to
current traffic patterns by giving high priority vehicles faster
routes to their destinations than low priority vehicles. For
example, the proactive routing instructions could route a low
priority vehicle to a side street in order to open a main road for
a high priority vehicle. A high priority vehicle is one that needs
to arrive at its destination as quickly as possible, such as an
emergency response vehicle or a vehicle driven by a surgeon on the
way to the hospital. By contrast, a low priority vehicle is one
that does not urgently need to reach its destination, such as one
driven by an individual heading to the shopping mall or to a
routine day at work. The primary vehicle 202 may be either a high
priority vehicle or a low priority vehicle. Similarly, the one or
more secondary vehicle(s) 210 may be either high priority or low
priority.
[0046] Not all vehicles encountered by the primary vehicle 202 may
necessarily be autonomous vehicles, semi-autonomous vehicles, or
non-autonomous vehicles provided with the disclosed technology.
Vehicles lacking wireless transceivers and automation technology
cannot receive transmission from a primary vehicle 202 and may not
be detectable by some embodiments. In other embodiments, for a
primary vehicle 202 with adequate sensor data available from the
on-board diagnostics system 500, the autonomous vehicle network
integration apparatus 300 may calculate an estimated secondary
influence vector based on motion of a secondary vehicle(s) 210
detected by the primary vehicle 202 sensors.
[0047] The data illustrated as moving between the autonomous
vehicle network integration apparatus 300 and the mobile device 206
may alternately be gathered from and transmitted to an autonomous
vehicle network integration data management center 212 over a
wireless network. The autonomous vehicle network integration data
management center 212 may be contacted using a wireless transceiver
configured as part of the autonomous vehicle network integration
apparatus 300, or using the wireless capabilities of the mobile
device 206, as indicated by the dotted lines. The autonomous
vehicle network integration data management center 212 may in turn
receive information from public, paid, and private third-party
databases 214. In some embodiments, the autonomous vehicle network
integration apparatus 300 may be configured to access the
third-party databases 214 directly. The autonomous vehicle network
integration apparatus 300 may communicate with the autonomous
vehicle network integration data management center 212 or other
entities using a dedicated communication channel, as illustrated in
FIG. 13, or over a mesh network 1400 as illustrated in FIG. 14.
[0048] FIG. 3 illustrates an autonomous vehicle network integration
apparatus 300 in accordance with one embodiment. The autonomous
vehicle network integration apparatus 300 comprises a wireless
transceiver 302, an on-board diagnostics connection port 304, a
memory 310, a central processing unit 312, and a bus 322. In some
embodiments, an on-board diagnostics wireless transceiver 306 may
be incorporated. Some embodiments may also comprise a universal
serial bus port 308.
[0049] In some embodiments, autonomous vehicle network integration
apparatus 300 may include many more components than those shown in
FIG. 3. However, it is not necessary that all of these generally
conventional components be shown in order to disclose an
illustrative embodiment. Collectively, the various tangible
components or a subset of the tangible components may be referred
to herein as "logic" configured or adapted in a particular way, for
example as logic configured or adapted with particular software or
firmware. In various embodiments, autonomous vehicle network
integration apparatus 300 may comprise one or more physical and/or
logical devices that collectively provide the functionalities
described herein. In some embodiments, autonomous vehicle network
integration apparatus 300 may comprise one or more replicated
and/or distributed physical or logical devices.
[0050] The wireless transceiver 302 may provide an interface to
communication networks and devices external to the autonomous
vehicle network integration apparatus 300. The wireless transceiver
302 may serve as an interface for receiving data from and
transmitting data to other systems. Embodiments of the wireless
transceiver 302 Bluetooth or WiFi, a near field communication
wireless interface, a cellular interface, and the like. The
wireless transceiver 302 may be coupled to a wireless communication
network via an antenna, either external to or integrated into the
printed circuit board comprising the wireless transceiver 302. The
wireless transceiver 302 may be used to communicate with a mobile
device within the primary vehicle, secondary vehicles encountered
during driving, an autonomous vehicle network integration data
management center, and other wireless entities within range.
[0051] The on-board diagnostics connection port 304 use a wired
connection to the primary vehicle's OBD connector, normally located
beneath the steering wheel. In these embodiments, the autonomous
vehicle network integration apparatus 300 may also be mounted
beneath the steering console of the primary vehicle, or it may be
mounted elsewhere in the primary vehicle with a cable running to
the steering console area. The cable may be a 16 pin pass through
cable or another OBD compatible hard-wired cable. In some
embodiments, the autonomous vehicle network integration apparatus
300 may include at least one universal serial bus port 308. A wired
OBD connection may be made using an OBD to USB cable. Other ports
may be included to allow alternate OBD connections. In some
embodiments, a wireless dongle may be connected to the primary
vehicle's OBD connector. This dongle may provide wireless
communication between the autonomous vehicle network integration
apparatus 300 and the on-board diagnostics system 500. The dongle
may include an on-board diagnostics wireless transceiver 306 that
communicates with the wireless transceiver 302.
[0052] At least one universal serial bus port 308 may be included
in some embodiments. The universal serial bus port 308 may be used
to connect to the on-board diagnostics system 500 as already
described. Alternately, the universal serial bus port 308 may
provide charging and data transfer capabilities to one or more
mobile devices within the primary vehicle.
[0053] Memory 310 generally comprises a random access memory
("RAM") and permanent non-transitory mass storage device, such as a
hard disk drive or solid-state drive. The memory 310 may store
instructions configured to implement a basic operating system 314
for the autonomous vehicle network integration apparatus 300. The
memory 310 may also comprise application instructions 316 which
configure the autonomous vehicle network integration apparatus 300
to implement the method disclosed herein. In some embodiments the
memory 310 may be used to store the vehicle profile 318 and one or
more driver profiles 320.
[0054] The central processing unit 312 may be configured to
implement logic comprising an on-board diagnostic data analyzer
324, a route analyzer 326, an influence vector calculator 328, a
range calculator 330, a secondary influence vector analyzer 332,
and a continuous guidance generator 334. The on-board diagnostic
data analyzer 324 may receive OBD data from the on-board
diagnostics connection port 304. The on-board diagnostic data
analyzer 324 may identify the OBD data needed to calculate the
influence vector, base influence range, and moving influence range.
These parameters may be sent to the 328 and the range calculator
330.
[0055] The route analyzer 326 may receive
destination/location/route information 226, traffic conditions 228,
and environmental conditions 230 via the wireless transceiver 302.
It may then identify the features of this data that affect the
influence vector and base influence range. These parameters may be
sent to the influence vector calculator 328 and the range
calculator 330.
[0056] The influence vector calculator 328 may dynamically
calculate an influence vector throughout primary vehicle operation.
The influence vector may be updated based on changes in velocity,
acceleration, deceleration, engine power, fuel levels, and similar
parameters, as indicated by OBD data analyzed by the on-board
diagnostic data analyzer 324. The influence vector may also be
updated based on changing route and environmental conditions, as
indicated by the route analyzer 326. The influence vector
calculator 328 provides the influence vector to the range
calculator 330 in order to calculate moving ranges.
[0057] The range calculator 330 may calculate a base influence
range based on vehicle profile 318 and driver profiles 320 received
from the memory 310 or via the wireless transceiver 302. In some
embodiments, the range calculator 330 may be configured to also
calculate a base awareness range and/or a base hazard range, as
described in further detail with regard to FIG. 7. The range
calculator 330 further accepts as input the influence vector
calculated by the influence vector calculator 328. The influence
vector is used generate a moving influence range which may take
into account the base influence range, and may modify it based on
the magnitude of the influence vector. The moving influence range
may be used to determine which secondary vehicles will influence
and be influenced by the primary vehicle as the primary vehicle
navigates the route, as illustrated in FIG. 6. In some embodiments,
a base hazard range and/or a base awareness range may be modified
with the influence vector to create a moving hazard range and/or a
moving awareness range.
[0058] The bus 322 provides an internal means of connection for the
separate electrical components comprised in the autonomous vehicle
network integration apparatus 300. This may be a universal serial
bus, a PCI or PCIe bus, or another bus technology configured to
carry signals between these components.
[0059] FIG. 4 illustrates an autonomous vehicle network integration
application user interface 400 in accordance with one embodiment.
The autonomous vehicle network integration application user
interface 400 may be displayed on a mobile device 206 and may
comprise a home icon 402, a profile icon 404, an audibles icon 406,
a settings icon 408, a driver profile selection 410, a vehicle
profile selection 412, a routing menu 414, an enter full screen
guidance mode 416, and a route summary 418. The mobile device 206
may be a cell phone, a tablet computer, a GPS navigation device, an
interface to a smart glass windshield display, or some other
technology able to provide an interactive user experience.
[0060] The home icon 402, profile icon 404, audibles icon 406, and
settings icon 408 may provide quick access to various screens
configured into the autonomous vehicle network integration
application. The home icon 402 may return the driver to a home
screen such as the one illustrated. The profile icon 404 may take
the driver to screen allowing them to select, view, or update their
driver profile, or may allow the driver to select a custom
configured set of screens associated with their driver profile. The
audibles icon 406 may provide a short cut to a screen where audible
alerts may be muted, volume-adjusted, and/or configured for
different actions or hazards. The settings icon 408 may take the
driver to a screen where all application settings may be viewed and
modified.
[0061] The driver profile selection 410 and vehicle profile
selection 412 selection bars may allow a driver to view and select
from a preconfigured menu of stored driver and vehicle profiles.
Alternately, the vehicle profile selection 412 may be programmed
into and automatically detected from the autonomous vehicle network
integration apparatus 300. The vehicle profile selection 412 may
then allow the driver to view details that comprise the vehicle
profile.
[0062] The routing menu 414 may allow the driver to set a
destination and calculate a route. It may present a set of options
to be selected by the driver that may control how the route is
determined and what continuous guidance is required or preferred.
The enter full screen guidance mode 416 bar may take the user to a
screen in which continuous guidance is provided visually, by means
of at least one of icons, brief statements, and various color
palettes (e.g., green for go faster or route clear and red for
hazard detected). The route summary 418 may provide an overview of
information on the expected trip, as shown, including distance,
weather, and estimated arrival.
[0063] FIG. 5 illustrates a basic on-board diagnostics system 500
in accordance with one embodiment. The on-board diagnostics system
500 comprises an electronic controller unit 502 (ECU) that collects
and manages diagnostic signals from a number of vehicle systems,
and an OBD connector 504 that may be used to connect the autonomous
vehicle network integration apparatus 300 to the on-board
diagnostics system 500. These vehicle systems may include the drive
train 506, the pedals 508, the steering 510, the exhaust system
512, the brakes 514, the engine 516, and the vehicle sensors
518.
[0064] The engine 516 may provide engine power 520 and deceleration
522 information. The brakes 514 may provide braking 524 data. The
vehicle sensors 518 may provide signaling 526 information, and in
some cases environmental conditions and secondary vehicles. The
steering 510 may provide information on turn 528 and lane excursion
530 events. The pedals 508, as well as the engine 516, may provide
information on acceleration 532. This data may be obtained from a
number of systems in a standard road transport vehicle. This
description is provided as one embodiment that may be used to
implement the disclosed solution.
[0065] On-board diagnostic data 216 sent to the autonomous vehicle
network integration apparatus 300 may be used for hazardous driving
detection 534. Velocity in excess of a speed limit along the route,
as indicated by databases or routing applications, may be one of
the driver actions detected and logged. Sharp braking 524 may be
detected. A sharp turn 528 or erratic motion of the steering 510
wheel may be logged, along with turning without signaling 526. Lane
excursion 530 may be detected through analysis of several data
sources, including location information and steering 510 and
signaling 526 data. These and other indicators may be defined as
hazardous driving, and a driver profile may in some embodiments be
updated to indicate that greater precautions may be necessary for
the indicated driver.
[0066] FIG. 6 illustrates influence vector and ranges 600 in
accordance with one embodiment. A primary vehicle 202 equipped with
an autonomous vehicle network integration apparatus 300 may have a
base influence range 602 established based on its vehicle profile,
the driver profile, and environmental conditions anticipated along
a detected route. The base influence range 602 may be considered
the smallest possible influence range, in effect regardless of the
vehicle's motion along the route. For this reason, the base
influence range 602 may in some embodiments effectively be a
perimeter at a distance equidistant from all points along the
surface of a vehicle. The illustrated embodiment depicts the base
influence range 602 as an oval, but other configurations are
possible.
[0067] The influence vector 604 may be calculated based on
parameters relating to the physical configurations of the primary
vehicle 202 and its motion. Acceleration, deceleration, and
velocity may impact the influence vector 604. A example with regard
to velocity is illustrated in FIG. 9. The mass of the primary
vehicle 202 may also be used in calculating the influence vector,
because the vehicle's mass, along with its velocity, will determine
its momentum, based on the formula in Equation 1. A vehicle's
momentum has a direct impact on its ability to brake to a complete
stop from a particular velocity. An illustration of how mass may
impact the influence vector is illustrated in FIG. 10.
momentum=mass.times.velocity Equation 1
[0068] The moving influence range 606 may be generated based on the
base influence range 602 and the influence vector 604. As
illustrated, the moving influence range 606 may extend into the
direction the primary vehicle 202 is moving, but may not extend as
far in the opposite direction. This configuration allows the
primary vehicle 202 to influence secondary vehicle(s) 210 that lie
near or within its anticipated field of motion. Thus, these
vehicles are those most likely to pose a hazard or induce route
adjustment. Once a secondary vehicle(s) 210 has been passed and
lies outside the primary vehicle 202 anticipated field of motion,
its influence and the hazard it might pose may drop off sharply,
and thus it may be excluded from the moving influence range
606.
[0069] FIG. 7 illustrates base range levels 700 in accordance with
one embodiment. In some configurations, it may be useful for a
primary vehicle 202 to detect or contact secondary vehicle(s) 210
that are nearby, but outside of its range of influence. For this
reason, a base awareness range 702 may be established. The base
awareness range 702 may typically lie outside of the base influence
range 602. The base awareness range 702 may be calculated as
proportional to the base influence range 602 (e.g., its radius may
be 120% of the base influence range 602 radius in all directions,
some other percentage, or some other shape). The base awareness
range 702 may in some embodiments be defined as an absolute
distance from the primary vehicle 202 (e.g., a fifty-yard
perimeter).
[0070] In some configurations, it may be useful for a primary
vehicle 202 to have a particular awareness of objects and events
occurring at a distance such that immediate and significant
response is necessary. For this reason, a base hazard range 704 may
be established. The base hazard range 704 may typically lie within
the base influence range 602. The base hazard range 704 may be
calculated as proportional to the base influence range 602 (e.g.,
its radius may be 40% of the base influence range 602 radius in all
directions, some other percentage, or some other shape). The base
hazard range 704 may in some embodiments be defined as an absolute
distance from the primary vehicle 202 (e.g., a five-yard
perimeter).
[0071] FIG. 8 illustrates moving range levels 800 in accordance
with one embodiment. The influence vector may be used to calculate
a moving awareness range 802 and moving hazard range 804 in the
same manner as it may be used to calculate the moving influence
range 606. This calculation may result in a moving awareness range
802 and moving hazard range 804 extending substantially more in the
primary vehicle 202 direction of motion than in the opposite
direction in order to give priority to awareness and prevention of
potential hazards in front of the primary vehicle 202.
[0072] In some embodiments, the moving awareness range 802 and/or
moving hazard range 804 may be increased by the magnitude of the
influence vector beyond the perimeters of the base awareness range
and base hazard range, respectively, but may be more circular than
oval, and may be more centered on the vehicle, in a manner that
does not give priority to secondary vehicles or hazards in the
direction of motion. Other configurations are possible in other
embodiments, depending on the specific calculations performed.
[0073] FIG. 9 illustrates influence vector and range comparison by
velocity 900 in accordance with one embodiment. The purpose of the
influence vector and its use in calculating moving ranges is to
provide the primary vehicle adequate influence upon and from
secondary vehicles most likely to pose a hazard or obstacle, i.e.,
those that lie in the direction the primary vehicle is moving. The
influence vector and moving ranges may also allow vehicles and
obstacles that are less likely to affect the primary vehicle, i.e.,
those not in the primary vehicle's direction of motion, to have a
very low influence upon and be significantly less influenced by the
primary vehicle.
[0074] For this reason, the influence vector may need to be
adjusted based on factors that increase the potential for hazard in
the primary vehicle's direction of motion by impacting the primary
vehicle's ability to slow or stop to avoid a hazard. One such
factor is the velocity the primary vehicle is traveling at. A car
travelling at 35 MPH 902 may be able to slow or stop much more
easily and quickly than a car travelling at 65 MPH 904, assuming
the two cars are similar in mass and other physical and performance
parameters. In order to account for the increased difficulty in
slowing or stopping, the influence vector for 1.5 tons at 65 MPH
908 may be significantly greater in magnitude than the influence
vector for 1.5 tons at 35 MPH 906. As a result, the moving
influence range for 1.5 tons at 65 MPH 912 may extend farther in
front of the car travelling at 65 MPH 904 than the moving influence
range for 1.5 tons at 35 MPH 910 does for the car travelling at 35
MPH 902.
[0075] As a result, the car travelling at 65 MPH 904 may detect
secondary vehicles at a greater distance, and may thus be able to
influence them and be influenced by them earlier than would
otherwise be the case. This earlier influence may provide the
primary vehicle driver more time to react to continuous guidance,
and thus more time to slow or stop the primary vehicle to avoid or
mitigate upcoming hazards.
[0076] FIG. 10 illustrates an influence vector and range comparison
by mass 1000 in accordance with one embodiment. The purpose of the
influence vector and its use in calculating moving ranges is to
provide the primary vehicle adequate influence upon and from
secondary vehicles most likely to pose a hazard or obstacle, i.e.,
those that lie in the direction the primary vehicle is moving. The
influence vector and moving ranges may also allow vehicles and
obstacles that are less likely to affect the primary vehicle, i.e.,
those not in the primary vehicle's direction of motion, to have a
very low influence upon and be significantly less influenced by the
primary vehicle.
[0077] For this reason, the influence vector may need to be
adjusted based on factors that increase the potential for hazard in
the primary vehicle's direction of motion by impacting the primary
vehicle's ability to slow or stop to avoid a hazard. One such
factor is the mass of the primary vehicle, or the weight of the
primary vehicle, as being directly proportional to its mass. A car
travelling at 45 MPH 1002 may be able to slow or stop much more
easily and quickly than a semi travelling at 45 MPH 1004. In order
to account for the increased difficulty in slowing or stopping, the
influence vector for 40 tons at 45 MPH 1008 (a potential weight for
a semi hauling a load) may be significantly greater in magnitude
than the influence vector for 1.5 tons at 45 MPH 1006 (a potential
weight for a 4 door passenger vehicle). As a result, the moving
influence range for 40 tons at 45 MPH 1012 may extend farther in
front of the semi travelling at 45 MPH 1004 than the moving
influence range for 1.5 tons at 45 MPH 1010 does for the car
travelling at 45 MPH 1002.
[0078] As a result, the semi travelling at 45 MPH 1004 may detect
secondary vehicles at a greater distance, and may thus be able to
influence them and be influenced by them earlier than would
otherwise be the case. This earlier influence may provide the
primary vehicle driver more time to react to continuous guidance,
and thus more time to slow or stop the primary vehicle to avoid or
mitigate upcoming hazards.
[0079] FIG. 11 illustrates routing related decisions 1100 in
accordance with one embodiment. When determining a route 1102 to a
selected destination 1104, the autonomous vehicle network
integration apparatus may collect data about traffic control 1106,
environmental conditions 1108, and traffic conditions 1110 along
the route. This data may continue to be collected as the vehicle
navigates to the destination, and continuous guidance, including
route navigation guidance, may be provided throughout the trip.
[0080] In one embodiment, traffic control 1106 data may indicate a
series of synchronized stoplights along the route 1102. Route
navigation guidance 1112 may be provided as the primary vehicle
approaches the first point of traffic control 1106, to indicate an
adjustment of speed that would align the vehicle's arrival at each
traffic light with the light's green cycle, allowing the primary
vehicle to pass through all of the lights without stopping.
[0081] In one embodiment, environmental conditions 1108 may be
detected once the route 1102 is calculated. For example, heavy
rain, fog, ice, or other conditions that may affect the driver's
ability to recognize hazards and the primary vehicle's ability to
slow and stop to avoid them, might be indicated. This data may by
used to effect a moving influence range increase due to weather
hazards 1116. Secondary vehicles may be detected earlier due to the
expanded moving influence range, thus allowing the driver more time
to adjust to their influence safely.
[0082] In one embodiment, traffic conditions 1110 may require a
route change due to road closure 1118 while the primary vehicle is
en route. Route navigation guidance 1114 may be provided to notify
the driver of a change in route 1102 and a new estimated arrival
time.
[0083] FIG. 12 illustrates hazard and secondary influence decisions
1200 in accordance with one embodiment. As a primary vehicle 202
navigates a route, a number of entities may fall within the moving
influence range 1202 of the primary vehicle 202. The solution
disclosed herein may provide hazard avoidance guidance 1210 and/or
secondary influence guidance 1212 to the driver based on these
entities.
[0084] In one embodiment, a traffic condition database may provide
the autonomous vehicle network integration apparatus data about
road hazards in the form at least one obstacle 1204 in the road a
primary vehicle 202 needs to traverse. Alternately, available
vehicle sensors may provide OBD data that would alert the driver to
the presence of an obstacle 1204 ahead. In either case, hazard
avoidance guidance 1210 may be provided to warn the driver of the
obstacle 1204 ahead.
[0085] In one embodiment, a traffic control database may provide
the autonomous vehicle network integration apparatus data about
traffic control 1206 entities, such as stoplights or, as
illustrated, yield signs. As the primary vehicle 202 approaches a
portion of the route controlled by a traffic control 1206 entity,
detecting a secondary vehicle(s) 210 within the moving influence
range 1202, having a secondary influence vector 1208 within the
moving influence range 1202, may trigger secondary influence
guidance 1212. For example, the primary vehicle 202 may have a
yield sign while the secondary vehicle(s) 210 does not. In such a
case, the secondary influence guidance 1212 may notify the driver
of a need to slow down and yield to the approaching secondary
vehicle(s) 210.
[0086] FIG. 13 illustrates a dedicated communication channel 1300
in accordance with one embodiment. In one embodiment, a
non-autonomous vehicle 1302, a non-autonomous vehicle 1304, and a
non-autonomous vehicle 1306, all configured to implement the
disclosed solution, may each communicate with an autonomous vehicle
network integration data management center 212 over a dedicated
communication channel. These autonomous vehicle network integration
communications uplink 1324 may occur directly with a dedicated AVNI
network, and be completely independent of and private from
vehicle-to-vehicle signaling 1326.
[0087] The autonomous vehicle network integration communications
uplink 1324 signals may be transmitted by non-autonomous vehicle
1302 to a satellite 1320 overhead. The satellite 1320 may belong to
a network of satellites used by cellular service providers,
internet service providers, or other entities, such as low earth
orbit satellites deployed by Amazon and Space X. The satellite 1320
receiving the autonomous vehicle network integration communications
uplink 1324 may transmit the signal to an earthbound communications
tower 1322, which may direct the transmission to the autonomous
vehicle network integration data management center 212 over a wide
area network, a local area network, the Internet, or some other
connection method. The autonomous vehicle network integration data
management center 212 may in turn transmit data to a communications
tower 1322 or satellite 1320, to be relayed to and received by the
non-autonomous vehicle 1302.
[0088] Non-autonomous vehicle 1302, non-autonomous vehicle 1304,
and non-autonomous vehicle 1306 may interact with the network of
autonomous vehicles they encounter, such as the illustrated
autonomous vehicle 1308, autonomous vehicle 1310, autonomous
vehicle 1312, autonomous vehicle 1314, autonomous vehicle 1316, and
autonomous vehicle 1318. Vehicle-to-vehicle signaling 1326 may be
transmitted by each vehicle to the other vehicles within its
wireless range. In this embodiment, however, the transmission of a
particular vehicle, such as non-autonomous vehicle 1302 may be
received by another within range, such as autonomous vehicle 1318,
but that transmission may not be relayed to other vehicles within
the range of autonomous vehicle 1318, such as autonomous vehicle
1314.
[0089] FIG. 14 illustrates a mesh network 1400 in accordance with
on embodiment. In contrast with the network configuration
illustrated in FIG. 13, communication between non-autonomous
vehicles as disclosed herein with the autonomous vehicle network
integration data management center 212 may be transmitted to any
wireless transceiver within range, and from thence be relayed
across multiple transceivers or "nodes" as a means of communicating
between the source and recipient of a signal transmission.
[0090] In one embodiment, non-autonomous vehicle 1302 may not be
able to detect and synchronize with satellite 1320. Rather than
needing to connect directly to a dedicated AVNI communication
channel, in a mesh network signaling 1404 configuration,
non-autonomous vehicle 1302 may be able to make use of autonomous
vehicle 1318 to relay the wireless transmission to communications
tower 1406, which may then transmit the signal to satellite 1320,
may send the signal over a wired (e.g. copper cable or fiber optic)
network, or may otherwise route the signal to the autonomous
vehicle network integration data management center 212.
[0091] Similarly, a transmission from non-autonomous vehicle 1304
may be relayed by wireless hotspot 1402 to communications tower
1322 and thus to the autonomous vehicle network integration data
management center 212. Non-autonomous vehicle 1306 may reach
autonomous vehicle network integration data management center 212
through a relayed transmission from autonomous vehicle 1310.
[0092] Relayed signaling across a mesh network may be managed by
logic incorporated within each wireless transceiver in the mesh
network. Wireless transceiver(s) within an autonomous vehicle
network integration apparatus may be configured to integrate with
public wireless hotspots, cellular and wireless towers, similarly
equipped vehicles, etc. In one embodiment, algorithms may limit the
number of nodes a signal may travel across. In one embodiment,
certain nodes such as communications towers for the driver's
cellular provider, or AVNI-equipped vehicles, may be preferred.
[0093] Terms used herein should be accorded their ordinary meaning
in the relevant arts, or the meaning indicated by their use in
context, but if an express definition is provided, that meaning
controls.
[0094] "Circuitry" in this context refers to electrical circuitry
having at least one discrete electrical circuit, electrical
circuitry having at least one integrated circuit, electrical
circuitry having at least one application specific integrated
circuit, circuitry forming a general purpose computing device
configured by a computer program (e.g., a general purpose computer
configured by a computer program which at least partially carries
out processes or devices described herein, or a microprocessor
configured by a computer program which at least partially carries
out processes or devices described herein), circuitry forming a
memory device (e.g., forms of random access memory), or circuitry
forming a communications device (e.g., a modem, communications
switch, or optical-electrical equipment).
[0095] "Firmware" in this context refers to software logic embodied
as processor-executable instructions stored in read-only memories
or media.
[0096] "Hardware" in this context refers to logic embodied as
analog or digital circuitry.
[0097] "Logic" in this context refers to machine memory circuits,
non transitory machine readable media, and/or circuitry which by
way of its material and/or material-energy configuration comprises
control and/or procedural signals, and/or settings and values (such
as resistance, impedance, capacitance, inductance, current/voltage
ratings, etc.), that may be applied to influence the operation of a
device. Magnetic media, electronic circuits, electrical and optical
memory (both volatile and nonvolatile), and firmware are examples
of logic. Logic specifically excludes pure signals or software per
se (however does not exclude machine memories comprising software
and thereby forming configurations of matter).
[0098] "Software" in this context refers to logic implemented as
processor-executable instructions in a machine memory (e.g.
read/write volatile or nonvolatile memory or media).
[0099] Herein, references to "one embodiment" or "an embodiment" do
not necessarily refer to the same embodiment, although they may.
Unless the context clearly requires otherwise, throughout the
description and the claims, the words "comprise," "comprising," and
the like are to be construed in an inclusive sense as opposed to an
exclusive or exhaustive sense; that is to say, in the sense of
"including, but not limited to." Words using the singular or plural
number also include the plural or singular number respectively,
unless expressly limited to a single one or multiple ones.
Additionally, the words "herein," "above," "below" and words of
similar import, when used in this application, refer to this
application as a whole and not to any particular portions of this
application. When the claims use the word "or" in reference to a
list of two or more items, that word covers all of the following
interpretations of the word: any of the items in the list, all of
the items in the list and any combination of the items in the list,
unless expressly limited to one or the other. Any terms not
expressly defined herein have their conventional meaning as
commonly understood by those having skill in the relevant
art(s).
[0100] Various logic functional operations described herein may be
implemented in logic that is referred to using a noun or noun
phrase reflecting said operation or function. For example, an
association operation may be carried out by an "associator" or
"correlator". Likewise, switching may be carried out by a "switch",
selection by a "selector", and so on.
[0101] As used herein, a recitation of "and/or" with respect to two
or more elements should be interpreted to mean only one element, or
a combination of elements. For example, "element A, element B,
and/or element C" may include only element A, only element B, only
element C, element A and element B, element A and element C,
element B and element C, or elements A, B, and C. In addition, "at
least one of element A or element B" may include at least one of
element A, at least one of element B, or at least one of element A
and at least one of element B. Further, "at least one of element A
and element B" may include at least one of element A, at least one
of element B, or at least one of element A and at least one of
element B
[0102] The subject matter of the present disclosure is described
with specificity herein to meet statutory requirements. However,
the description itself is not intended to limit the scope of this
disclosure. Rather, the inventors have contemplated that the
claimed subject matter might also be embodied in other ways, to
include different steps or combinations of steps similar to the
ones described in this document, in conjunction with other present
or future technologies. Moreover, although the terms "step" and/or
"block" may be used herein to connote different elements of methods
employed, the terms should not be interpreted as implying any
particular order among or between various steps herein disclosed
unless and except when the order of individual steps is explicitly
described.
* * * * *