Group driving style learning framework for autonomous vehicles

Patent metadata

Field	Value
Publication number	US-11238733-B2
Application number	US-201816040494-A
Country	US
Kind code	B2
Filing date	Jul 19, 2018
Priority date	Oct 13, 2016
Publication date	Feb 1, 2022
Grant date	Feb 1, 2022

Abstract

A social driving style learning framework or system for autonomous vehicles is utilized, which can dynamically learn the social driving styles from surrounding vehicles and adopt the driving style as needed. Each of the autonomous vehicles within a particular driving area is equipped with the driving style learning system to perceive the driving behaviors of the surrounding vehicles to derive a set of driving style elements. Each autonomous vehicle transmits the driving style elements to a centralized remote server. The server aggregates the driving style elements collected from the autonomous vehicles to determine a driving style corresponding to that particular driving area. The server transmits the driving style back to each of the autonomous vehicles. The autonomous vehicles can then decide whether to adopt the driving style, for example, to follow the traffic flow with the rest of the vehicles nearby.

First claim

What is claimed is:

1. A non-transitory machine-readable medium storing instructions, which when executed by a processor, cause the processor to perform operations of operating an autonomous vehicle, the operations comprising:
perceiving, by a first autonomous vehicle, driving behaviors of one or more first surrounding vehicles, each of the one or more first surrounding vehicles surrounding the first autonomous vehicle;
determining, for each of the one or more first surrounding vehicles, first information describing one or more driving style elements based on a driving behavior of the surrounding vehicle;
transmitting second information describing a first set of driving style elements representing driving behaviors of the one or more first surrounding vehicles from the first autonomous vehicle to a second autonomous vehicle over a wireless network, the second information comprising some or all of the first information;
receiving third information describing a second set of driving style elements from the second autonomous vehicle, the second set of driving style elements determined by the second autonomous vehicle based on a perception of driving behaviors of one or more second surrounding vehicles surrounding the second autonomous vehicle;
determining a driving style based on at least some of the second information and at least some of the third information, wherein the driving style includes fourth information describing how the first autonomous vehicle should drive at a point in time in view of the one or more first surrounding vehicles, the one or more second surrounding vehicles, or both; and
operating the first autonomous vehicle based on planning and control data generated based, at least in part, on the driving style, wherein operating the first autonomous vehicle comprises controlling the first autonomous vehicle, driving the first autonomous vehicle, or both.

2. The non-transitory machine-readable medium of claim 1 , wherein the first autonomous vehicle and the second autonomous vehicle are located within a predetermined proximity at the point in time.

3. The non-transitory machine-readable medium of claim 1 , wherein the operations further comprise transmitting a location of each of the one or more first surrounding vehicles surrounding the first autonomous vehicle to the second autonomous vehicle, wherein the second set of driving style elements is determined based on locations of the one or more first surrounding vehicles.

4. The non-transitory machine-readable medium of claim 1 , wherein the operations further comprise receiving a location of each of the one or more second surrounding vehicles surrounding the second autonomous vehicle, wherein determining the driving style is further based on locations of the one or more second surrounding vehicles surrounding the second autonomous vehicle.

5. The non-transitory machine-readable medium of claim 1 , wherein the first set of driving style elements, the second set of driving style elements, or both comprises at least one selected from a group consisting of:
a driving speed associated with the one or more first surrounding vehicles or the one or more second surrounding vehicles,
a distance between a plurality of the one or more first surrounding vehicles,
a distance between a plurality of the one or more second surrounding vehicles,
a distance between one of the one or more first surrounding vehicles and one of the one or more second surrounding vehicles,
a deceleration rate associated with the one or more first surrounding vehicles or the one or more second surrounding vehicles, and
a distance of deceleration associated with the one or more first surrounding vehicles or the one or more second surrounding vehicles.

6. The non-transitory machine-readable medium of claim 5 , wherein the first set of driving style elements, the second set of driving style elements, or both further comprises at least one selected from a group consisting of:
a frequency of lane changes,
a speed of a lane change, and
a turning angle of a lane change.

7. A computer-implemented method for operating autonomous driving vehicles, the method comprising:
receiving, at a server, first information describing one or more driving style elements associated with a plurality of autonomous driving vehicles (ADVs) over a network;
determining one or more driving areas based on locations of the plurality of ADVs; and
for each of the one or more driving areas,
identifying a first set of ADVs that are within a predetermined proximity of the driving area, the first set of ADVs comprising one or more ADVs from the plurality of ADVs,
performing an analysis on second information describing one or more driving style elements that are associated with the first set of ADVs, the first information comprising the second information,
determining a driving style corresponding to the driving area based on the analysis, and
transmitting the driving style to a second set of ADVs that are within the predetermined proximity of the driving area, the second set of ADVs comprising one or more ADVs from the plurality of ADVs and the driving style including third information describing how each ADV from the second set of ADVs should drive in the driving area.

8. The method of claim 7 , wherein determining a driving style corresponding to the driving area based on the analysis comprises:
determining a current speed of ADV in the first set based on some or all of the second information; and
calculating an average speed for all ADVs in the first set based on current speeds of all ADVs in the first set.

9. The method of claim 7 , wherein determining a driving style corresponding to the driving area based on the analysis comprises:
determining a distance between each pair of adjacent ADVs in the first set based on some or all of the second information; and
calculating an average distance of the for all pairs of adjacent ADVs in the first set based on [[the ]]distances between all pairs of adjacent ADVs in the first set.

10. The method of claim 7 , wherein determining a driving style corresponding to the driving area based on the analysis comprises:
determining a deceleration rate of each ADV in the first set based on some or all of the second information; and
calculating an average deceleration rate for all ADVs in the first set based on deceleration rates of all ADVs in the first set.

11. The method of claim 10 , further comprising:
determining a deceleration distance of each ADV in the first set based on the deceleration rate ADV; and
calculating an average deceleration distance for all ADVs in the first set based on deceleration distances of all ADVs in the first set.

12. The method of claim 7 , wherein determining a driving style corresponding to the driving area based on the analysis comprises:
determining a number of lane changes of each of the ADV in the first set based on some or all of the second information; and
calculating an average number of lane changes for all ADVs in the first set based on numbers of lane changes of all ADVs in the first set.

13. The method of claim 12 , further comprising determining an average speed of lane changes for all ADVs in the first set based on some or all of the second information.

14. The method of claim 12 , further comprising determining an average turning angle of lane changes for all ADVs in the first set based on some or all of the second information.

15. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations of operating autonomous dri

Assignees

• Baidu Usa Llc

Inventors

• Jiang Yifei
• Li Dong
• Tao Jiaming
• Hu Jiangtao
• Li Liyun
• Yang Guang
• Wang Jingao

Classifications

• G08G1/0129

  for creating historical data or processing based on historical data · CPC title

• B60W2556/65

  Data transmitted between vehicles · CPC title

• B60W2556/45

  External transmission of data to or from the vehicle · CPC title

• G08G1/0112

  from the vehicle, e.g. floating car data [FCD] · CPC title

• G08G1/096725 Primary

  where the received information generates an automatic action on the vehicle control · CPC title