Adaptive route and motion planning based on learned external and internal vehicle environment

• US 10,234,302 B2
• Filed: 06/27/2017
• Issued: 03/19/2019
• Est. Priority Date: 06/27/2017
• Status: Active Grant

• Current Assignee: NIO Technology Company Limited (Shanghai Anbin Technology Co., Ltd.)
• Original Assignee: Nio Usa Inc.
• Inventors: Singhal, Abhishek, Aharony, Nadav, Liang, Anthony Tao, Muralidhar, Gautam
• Primary Examiner(s): Camby, Richard M
• Application Number:
  US15/634,197
• Publication Number:
  US 20180372504A1
• Time in Patent Office: 630 Days
• Field of Search: None
• CPC Class Codes: B60N 2/002 Seats provided with an occupancy detection means mounted therein or thereon detection means responsive to presence or absence of children B60N2/266
• CPC Class Codes: G01C 21/3492 employing speed data or traffic data, e.g. real-time or historical traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G01C 21/3602 Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera
• CPC Class Codes: G01C 21/3691 Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions arrangements for giving variable traffic instructions G08G1/09
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/0145 for active traffic flow control
• CPC Class Codes: G08G 1/09623 Systems involving the acquisition of information from passive traffic signs by means mounted on the vehicle G08G1/0967 takes precedence
• CPC Class Codes: G08G 1/096827 where the route is computed onboard

Abstract

The systems and methods described herein can be applied to a vehicle equipped with a sensor suite that can observe information about a location, for example, a traffic light duration. The system can record information, e.g., the traffic light colors, duration of color changes, and location of the traffic lights and can upload this information to the cloud. Then, the system can augment or learn about the location, e.g., learning of traffic patterns, and store the augmented data as database-based information, where available. The learned information can help a requesting vehicle to better estimate an estimated time of arrival (ETA) for common routes taken, provide more accurate ETAs based on historical knowledge, and/or calculate or provide alternative route information.

Claims

1. A vehicle, comprising:

a first set of sensors to sense an environment surrounding the vehicle and to gather information about a location in which the vehicle is within physical proximity;

a second set of sensors to sense an inside of the vehicle, wherein the second set of sensors determines a number of occupants currently in the vehicle;

a vehicle control system to operate the vehicle in a high or a full automation level;

wherein the vehicle control system and/or a remote route engine, based on information gathered by first sets of sensors of multiple different vehicles, a number of occupants currently in the vehicle, and a destination selected by a current occupant of the vehicle, selects at least one of (a) from among multiple possible routes from the location and the selected destination, a route from the location to the selected destination, the selected route, relative to the other possible routes, being optimal for high or full automation level operation of the vehicle and (b) a set of maneuvers for the selected route to be executed by the vehicle control system while operating in the high or full automation level along the selected route, the set of maneuvers being optimal relative to a selected characteristic for the route.

2. The vehicle of claim 1, wherein the vehicle control system and/or a remote route engine selects option (a), wherein, when the vehicle control system operates the vehicle in a high or full automation level, the vehicle control system executes driving operations autonomously, the driving operations comprising steering, accelerating the vehicle, decelerating the vehicle, and braking the vehicle, wherein the optimal set of maneuvers comprises a lane change, a turn, an acceleration, and a deceleration, wherein the optimal set of maneuvers are selected are predetermined, and wherein the optimal set of maneuvers define an optimal driving pattern for the selected route.

3. The vehicle of claim 2, wherein the selected characteristic for the selected route is travel time and/or estimated time of arrival and wherein the gathered information comprises traffic light locations along the selected route, for each traffic light along the selected route, one or more of traffic light cycle time to change from a first light color to a second light color, and a duration of illumination of a selected traffic light color, and a plurality of a number of vehicles in each lane along a length of the selected route, a vehicle occupancy requirement for each lane along a length of the selected route, and a toll related rule for a length of the selected route.

4. The vehicle of claim 1, wherein the vehicle control system and/or a remote route engine selects option (b), wherein when the vehicle operates in the high automation level, a human driver is separated from controlling all vehicle operations and wherein a processor of the vehicle:

identifies the location within the environment;

collects metadata associated with the location; and

sends the collected metadata to a navigation source, wherein the metadata includes a characteristic about the location.

5. The vehicle of claim 4, wherein the location is an intersection, wherein the metadata comprises a number of lanes and, for each lane, a plurality of a type of lane, vehicle occupancy requirements to use the lane, how long a turn signal is active for each lane, and how many cars are in the lane, and a navigation source, in communication with the route engine, aggregates metadata associated with the location received in temporal proximity from multiple vehicles and wherein the metadata is used by the vehicle control system and/or route engine to select the optimal route and/or set of maneuvers.

6. The vehicle of claim 4, wherein the collected metadata is associated with a traffic light at the intersection and a navigation source, in communication with the route engine, aggregates metadata associated with the location received in temporal proximity from multiple vehicles and wherein the metadata is used by the vehicle control system and/or route engine to select the optimal route and/or set of maneuvers and wherein the collected metadata is a cycle time for the traffic light and/or a length of a green light.

7. The vehicle of claim 4, wherein the navigation source receives, from each of the multiple vehicles, collected metadata in association with a unique identifier of the corresponding vehicle, an identifier of the location associated with the collected metadata, a type of location of the identified location, a type of the metadata collected, and a date and timestamp of metadata collection and aggregates the collected metadata from the multiple vehicles by identified location.

8. The vehicle of claim 5, wherein a first portion of the collected metadata is a time to clear the intersection, a number of vehicles that clear the intersection during a green light, a number of vehicles that turn during a turn signal, and/or a number of vehicles in a lane during at particular time of day and/or day and a second portion of the collected metadata is associated with the high occupancy vehicle (HOV) lane.

9. The vehicle of claim 8, wherein the collected metadata is a number of occupants required to use the HOV lane, a number of occupants required during a particular time of day and/or day, an amount of traffic expected in an HOV lane based on a time of day or day of a week, and/or a time to travel in the HOV lane.

10. The vehicle of claim 9, wherein the processor:

receives a request to determine a route to a second location;

requests information, about a third location on the route, from the navigation source;

receives second metadata associated with the third location; and

determines the route based on the second metadata about the third location, wherein the third location is a second traffic light, and wherein the route avoids the second traffic light.

11. A method, comprising:

sensing, by a first set of sensors of a selected vehicle, an environment surrounding the vehicle and gathering information about a location in which the vehicle is within physical proximity;

sensing, by a second set of sensors of the selected vehicle, an inside of the vehicle, wherein the second set of sensors determines a number of occupants currently in the vehicle;

operating, by a vehicle control system, the selected vehicle in a high or a full automation level; and

selecting, by the vehicle control system and/or a remote route engine, based on information gathered by first sets of sensors of multiple different vehicles, a number of occupants currently in the vehicle, and a destination selected by a current occupant of the vehicle, one or more of (a) from among multiple possible routes from the location and the selected destination, a route from the location to the selected destination, the selected route, relative to the other possible routes, being optimal for high or full automation level operation of the vehicle and (b) a set of maneuvers for the selected route to be executed by the vehicle control system while operating in the high or full automation level along the selected route, the set of maneuvers being optimal relative to a selected characteristic for the route.

12. The method of claim 11, wherein the vehicle control system and/or a remote route engine selects option (b), wherein, when the vehicle control system operates the vehicle in a high or full automation level, the vehicle control system executes driving operations autonomously, the driving operations comprising steering, accelerating the vehicle, decelerating the vehicle, and braking the vehicle, wherein the optimal set of maneuvers comprises a lane change, a turn, an acceleration, and a deceleration, wherein the optimal set of maneuvers are selected are predetermined, and wherein the optimal set of maneuvers define an optimal driving pattern for the selected route.

13. The method of claim 12, wherein the selected characteristic for the selected route is travel time and/or estimated time of arrival and wherein the gathered information comprises traffic light locations along the selected route, for each traffic light along the selected route, one or more of traffic light cycle time to change from a first light color to a second light color, and a duration of illumination of a selected traffic light color, and a plurality of a number of vehicles in each lane along a length of the selected route, a vehicle occupancy requirement for each lane along a length of the selected route, and a toll related rule for a length of the selected route.

14. The method of claim 11, wherein the vehicle control system and/or a remote route engine selects option (a), wherein when the vehicle operates in the high automation level, a human driver is separated from controlling all vehicle operations and wherein a processor of the vehicle:

identifies the location within the environment;

collects metadata associated with the location; and

sends the collected metadata to a navigation source, wherein the metadata includes a characteristic about the location.

15. The method of claim 14, wherein the location is an intersection, wherein the metadata comprises a number of lanes and, for each lane, a plurality of a type of lane, vehicle occupancy requirements to use the lane, how long a turn signal is active for each lane, and how many cars are in the lane, and a navigation source, in communication with the route engine, aggregates metadata associated with the location received in temporal proximity from multiple vehicles and wherein the metadata is used by the vehicle control system and/or route engine to select the optimal route and/or set of maneuvers.

16. The method of claim 14, wherein the collected metadata is associated with a traffic light at the intersection and a navigation source, in communication with the route engine, aggregates metadata associated with the location received in temporal proximity from multiple vehicles and wherein the metadata is used by the vehicle control system and/or route engine to select the optimal route and/or set of maneuvers and wherein the collected metadata is a cycle time for the traffic light and/or a length of a green light.

17. The method of claim 14, wherein the navigation source receives, from each of the multiple vehicles, collected metadata in association with a unique identifier of the corresponding vehicle, an identifier of the location associated with the collected metadata, a type of location of the identified location, a type of the metadata collected, and a date and timestamp of metadata collection and aggregates the collected metadata from the multiple vehicles by identified location.

18. The method of claim 15, wherein a first portion of the collected metadata is a time to clear the intersection, a number of vehicles that clear the intersection during a green light, a number of vehicles that turn during a turn signal, and/or a number of vehicles in a lane during at particular time of day and/or day and a second portion of the collected metadata is associated with the high occupancy vehicle (HOV) lane.

19. The method of claim 18, wherein the collected metadata is a number of occupants required to use the HOV lane, a number of occupants required during a particular time of day and/or day, an amount of traffic expected in an HOV lane based on a time of day or day of a week, and/or a time to travel in the HOV lane.

20. The method of claim 19, further comprising:

receiving a request to determine a route to a second location;

requesting information, about a third location on the route, from the navigation source;

receiving second metadata associated with the third location; and

determining the route based on the second metadata about the third location, wherein the third location is a second traffic light, and wherein the route avoids the second traffic light.