Glare detection systems and methods for automated vehicular control

• US 10,083,606 B2
• Filed: 08/22/2016
• Issued: 09/25/2018
• Est. Priority Date: 08/22/2016
• Status: Active Grant

• Current Assignee: Allstate Insurance Company (Allstate Corporation)
• Original Assignee: Allstate Insurance Company (Allstate Corporation)
• Inventors: Krunic, Veljko, Gibson, Timothy W., Chintakindi, Sunil, Hayes, Howard
• Primary Examiner(s): INGRAM, THOMAS P
• Application Number:
  US15/243,421
• Publication Number:
  US 20180053415A1
• Time in Patent Office: 764 Days
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2420/54 Audio sensitive means, e.g. ultrasound
• CPC Class Codes: B60W 2552/05 Type of road, e.g. motorways, local streets, paved or unpaved roads
• CPC Class Codes: B60W 2552/10 Number of lanes
• CPC Class Codes: B60W 2552/15 Road slope, i.e. the inclination of a road segment in the longitudinal direction
• CPC Class Codes: B60W 2552/30 Road curve radius
• CPC Class Codes: B60W 2552/35 Road bumpiness, e.g. potholes
• CPC Class Codes: B60W 2554/20 Static objects
• CPC Class Codes: B60W 2554/4026 Cycles
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2554/406 Traffic density
• CPC Class Codes: B60W 2554/60 Traversable objects, e.g. speed bumps or curbs
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 40/02 related to ambient conditions
• CPC Class Codes: B60W 60/0053 from vehicle to occupant
• CPC Class Codes: B60W 60/0059 Estimation of the risk associated with autonomous or manual driving, e.g. situation too complex, sensor failure or driver incapacity
• CPC Class Codes: G01W 1/10 Devices for predicting weather conditions computers per se G06; display devices G09
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0129 : for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0133 : for classifying traffic situation
• CPC Class Codes: G08G 1/0145 : for active traffic flow control
• CPC Class Codes: G08G 1/091 : Traffic information broadcasting broadcasting communication H04H
• CPC Class Codes: G08G 1/096716 : where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096725 : where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 : where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/096775 : where the origin of the information is a central station
• CPC Class Codes: G08G 1/096791 : where the origin of the information is another vehicle
• CPC Class Codes: G08G 1/096816 : where the complete route is transmitted to the vehicle at once
• CPC Class Codes: G08G 1/096833 : where different aspects are considered when computing the route
• CPC Class Codes: G08G 1/096838 : where the user preferences are taken into account or the user selects one route out of a plurality
• CPC Class Codes: G08G 1/096844 : where the complete route is dynamically recomputed based on new data

Abstract

Aspects of the present disclosure describe systems, methods, and devices for automated vehicular control based on glare detected by an optical system of a vehicle. In some aspects, automated control includes controlling the operation of the vehicle itself, a vehicle subsystem, or a vehicle component based on a level of glare detected. According to some examples, controlling the operation of a vehicle includes instructing an automatically or manually operated vehicle to traverse a selected route based on levels of glare detected or expected along potentials routes to a destination. According to other examples, controlling operation of a vehicle subsystem or a vehicle component includes triggering automated responses by the subsystem or the component based on a level of glare detected or expected. In some additional aspects, glare data is shared between individual vehicles and with a remote data processing system for further analysis and action.

Claims

1. A vehicle control system comprising:

at least one processor; and

at least one memory storing computer-executable instructions that, when executed by the at least one processor, cause the system to;

determine a location of a first vehicle;

receive predefined navigational data from a map database, the predefined navigational data characterizing one or more route segments corresponding to the location of the first vehicle;

receive glare data associated with the one or more route segments, the glare data characterizing glare detected by at least a second vehicle at the one or more route segments within a predetermined recent time period and where the second vehicle is within a threshold distance to the determined location of the first vehicle;

determine, based on the predefined navigational data and the glare data, a glare factor for each route segment of the one or more route segments;

create a map comprising the one or more route segments and comprising, for each of the one or more route segments, the glare factor determined for that route segment; and

transmit, to the first vehicle, a command that is based on a corresponding glare factor determined for one of the one or more route segments of the map.

2. The system of claim 1, wherein:

the instructions when executed by the at least one processor, further cause the system to receive environmental data associated with the one or more route segments; and

the glare factor determined for each route segment of the one or more segments is further based on the environmental data.

3. The system of claim 2, wherein the environmental data comprises at least one of weather data, traffic data, time-of-day data, day-of-the week data, topographic data, or road data.

4. The system of claim 1, wherein the instructions, when executed by the at least one processor, further cause the system to update the map at preconfigured intervals based on new glare data detected along one of the one or more route segments of the map.

5. The system of claim 1, wherein the instructions, when executed by the at least one processor, further cause the system to:

receive, from the first vehicle, a location and a destination;

determine, based on the predefined navigational data, the location, and the destination, one or more possible routes from the location to the destination, each of the one or more possible routes comprising one of the one or more route segments of the map;

select, based on at least one glare factor of the map, one of the one or more possible routes as a recommended route; and

identify the recommended route in the command transmitted to the first vehicle.

6. The system of claim 5, wherein selecting the recommended route comprises comparing the at least one glare factor of the map to a glare factor threshold.

7. The system of claim 5, wherein the instructions, when executed by the at least one processor, further cause the system to transmit, to the first vehicle, a new command based on at least one of additional glare data or additional environmental data associated with at least one route segment of the recommended route, the new command identifying an update to the recommended route.

8. The system of claim 1, wherein receipt of the command at the first vehicle activates the vehicle to switch between a first operation state and a second operation state.

9. The system of claim 8, wherein the first operation state is an autonomous operation of the first vehicle by an autonomous vehicle control system of the vehicle and the second operation state is a manual operation of the first vehicle by a driver of the vehicle.

10. The system of claim 9, wherein the autonomous operation of the first vehicle includes automating at least one feature of vehicle operation.

11. A vehicle control system, comprising:

a first computing device comprising at least one processor and at least one memory storing computer-executable instructions that, when executed by the at least one processor, cause the first computing device to;

receive predefined navigational data from a map database;

receive, from a first vehicle, a first location and a destination;

determine, based on the predefined navigational data, the first location, and the destination, one or more possible routes from the location to the destination, each of the one or more possible routes comprising one or more route segments;

receive, for each possible route of the one or more possible routes, glare data detected by at least a second vehicle at each of the one or more route segments of the possible route within a predetermined recent time period and where the second vehicle is within a threshold distance to the first location of the first vehicle;

determine, based on the glare data, a glare factor for each of the one or more possible routes;

select, based on comparing each glare factor to a glare factor threshold, one of the one or more possible routes as a recommended route; and

transmit the recommended route to the first vehicle; and

a second computing device on-board the first vehicle, the second computing device being communicatively connected to the first computing device, comprising at least one processor and at least one memory storing computer-readable instructions that, when executed by the at least one processor, cause the second computing device to;

receive, from the first computing device, the recommended route.

12. The system of claim 11, wherein the instructions of the second computing device, when executed by the at least one processor of the second computing device, further cause the second computing device to:

display the recommended route on a display within the first vehicle.

13. The system of claim 12, wherein the instructions of the second computing device, when executed by the at least one processor of the second computing device, further cause the second computing device to change an operation state of the first vehicle based on glare data associated with a route segment of the recommended route.

14. The system of claim 13, wherein changing the operation state of the first vehicle comprises:

activating or deactivating an autonomous vehicle control system of the first vehicle.

15. The system of claim 11, wherein the glare factor threshold is a user-selected glare factor threshold and stored, by the first computing device, in a user profile associated with a driver of the first vehicle.

16. A method of controlling a vehicle, the method comprising:

determining, by the computing device, a location of a first vehicle;

storing, at a data store of a computing device, glare data characterizing glare detected by a plurality of vehicles traversing one or more route segments;

receiving, by the computing device, predefined navigational data from a map database, the predefined navigation data characterizing the one or more route segments;

receiving, by the computing device, environmental data associated with the one or more route segments;

configuring, by the computing device, a glare prediction model based on the glare data detected by at least a second vehicle and the environmental data, where the second vehicle is within a threshold distance to the determined location of the first vehicle;

receiving, by the computing device, current environmental data associated with a route segment;

generating, by the computing device with the glare prediction model, a current glare factor for the route segment; and

transmitting, by the computing device to a vehicle, a command that is based on the current glare factor.

17. The method of claim 16, wherein the environmental data comprises at least one of weather data, traffic data, time-of-day data, day-of-the-week data, topographic data, or road data.

18. The method of claim 17, wherein the current glare factor determined for the route segment is one of a plurality of glare factors determined for a plurality of route segments of a route along which the first vehicle is traveling.

19. The method of claim 18, further comprising:

generating, by the computing device, a glare factor map for the vehicle based on the plurality of route segments of the route and the plurality of glare factors determined for the plurality of route segments; and

displaying the glare factor map at a display within the first vehicle.

20. The method of claim 16, further comprising:

receiving, by the computing device, new environmental data;

generating, by the computing device with the glare prediction model, a new glare factor; and

transmitting, by the computing device to the first vehicle, a new command that is based on the new glare factor.