Sideslip compensated control method for autonomous vehicles

• US 10,019,008 B2
• Filed: 09/28/2016
• Issued: 07/10/2018
• Est. Priority Date: 09/28/2016
• Status: Active Grant

• Current Assignee: Baidu USA LLC (Baidu Incorporated)
• Original Assignee: Baidu USA LLC (Baidu Incorporated)
• Inventors: Kong, Qi, Zhu, Fan, Yang, Guang, Wang, Jingao
• Primary Examiner(s): Badii, Behrang
• Assistant Examiner(s): Testardi, David A
• Application Number:
  US15/279,141
• Publication Number:
  US 20180088582A1
• Time in Patent Office: 650 Days
• CPC Class Codes: B60W 2050/0031 Mathematical model of the vehicle
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2050/0088 Adaptive recalibration
• 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 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2520/105 Longitudinal acceleration
• CPC Class Codes: B60W 2520/20 Sideslip angle
• CPC Class Codes: B60W 2540/18 Steering angle
• CPC Class Codes: B60W 2552/05 Type of road, e.g. motorways, local streets, paved or unpaved roads
• CPC Class Codes: B60W 2552/30 Road curve radius
• CPC Class Codes: B60W 2552/53 Road markings, e.g. lane marker or crosswalk
• CPC Class Codes: B60W 2554/20 Static objects
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2554/80 Spatial relation or speed relative to objects
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2710/207 Steering angle of wheels
• CPC Class Codes: B60W 2720/20 Sideslip angle
• CPC Class Codes: B60W 30/045 : Improving turning performance
• CPC Class Codes: B60W 30/0956 : the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 30/18145 : Cornering
• CPC Class Codes: B60W 40/103 : Side slip angle of vehicle body
• CPC Class Codes: B60W 50/0097 : Predicting future conditions
• CPC Class Codes: B60W 60/0016 : of the vehicle or its occupants
• CPC Class Codes: B62D 15/025 : Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation B62D1/28 takes precedence
• CPC Class Codes: B62D 6/002 : computing target steering angles for front or rear wheels B62D7/159 takes precedence
• CPC Class Codes: G01C 21/3415 : Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
• 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: G05D 1/0022 : characterised by the communication link data switching networks in general H04L12/00
• CPC Class Codes: G05D 1/0212 : with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287

Abstract

A set of driving scenarios are determined for different types of vehicles. Each driving scenario corresponds to a specific movement of a particular type of autonomous vehicles. For each of the driving scenarios of each type of autonomous vehicles, a set of driving statistics is obtained, including driving parameters used to control and drive the vehicle, a driving condition at the point in time, and a sideslip caused by the driving parameters and the driving condition under the driving scenario. A driving scenario/sideslip mapping table or database is constructed. The scenario/sideslip mapping table includes a number of mapping entries. Each mapping entry maps a particular driving scenario to a sideslip that is calculated based on the driving statistics. The scenario/sideslip mapping table is utilized subsequently to predict the sideslip under the similar driving environment, such that the driving planning and control can be compensated.

Claims

1. A computer-implemented method for operating an autonomous vehicle, the method comprising:

defining a plurality of driving scenarios for a plurality of types of autonomous vehicles, each driving scenario corresponding to a specific movement of a particular type of autonomous vehicles;

for each of the driving scenarios of each type of autonomous vehicles, obtaining driving statistics of each autonomous vehicle, including

recording one or more driving parameters used to control and drive each autonomous vehicle of the type,

capturing a driving condition corresponding the driving scenario, and

calculating an average sideslip caused by the driving parameters and the driving condition based on sideslips of a number of autonomous vehicles of the type under identical driving conditions; and

storing a driving scenario to a sideslip (scenario/sideslip) mapping table having a plurality of mapping entries to map the driving scenario to the calculated average sideslip, wherein the scenario/sideslip mapping table is utilized to compensate a sideslip under a similar driving scenario, the method further including

determining a driving scenario based on a driving condition of an autonomous vehicle;

identifying an entry matching the driving scenario from the scenario/sideslip mapping table;

obtaining an average sideslip from the matching entry; and

adjusting a steering angle of the autonomous vehicle using the obtained average sideslip.

2. The method of claim 1, further comprising:

in response to determining a next movement of the autonomous vehicle, determining a first driving scenario corresponding to the next movement;

searching in the scenario/sideslip mapping table based on the first driving scenario to locate a mapping entry corresponding to a second driving scenario matching the first driving scenario; and

determining the next movement for the autonomous vehicle by taking into consideration of a first sideslip obtained from the located mapping entry.

3. The method of claim 2, wherein determining the next movement for the autonomous vehicle by taking into consideration of a first sideslip obtained from the located mapping entry comprises:

calculating a first steering angle based on a speed required by a route associated with the next movement using a predetermined algorithm;

modifying the first steering angle in view of the first sideslip obtained from the located mapping entry, generating a second steering angle; and

controlling the autonomous vehicle based on the second steering angle.

4. The method of claim 1, wherein the driving condition comprises at least one of a type of road or a weather condition.

5. The method of claim 1, wherein a driving parameter identifies an action an autonomous vehicle is taking, a speed during the action, or a steering angle during the action.

6. The method of claim 5, wherein the driving parameter further identifies a type of an autonomous vehicle involved.

7. The method of claim 5, wherein the action is one of a left turn, a right turn, or a U-turn.

8. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations of operating an autonomous vehicle, the operations comprising:

defining a plurality of driving scenarios for a plurality of types of autonomous vehicles, each driving scenario corresponding to a specific movement of a particular type of autonomous vehicles;

for each of the driving scenarios of each type of autonomous vehicles, obtaining driving statistics of each autonomous vehicle, including

recording one or more driving parameters used to control and drive each autonomous vehicle of the type,

capturing a driving condition corresponding the driving scenario, and

calculating an average, sideslip caused by the driving parameters and the driving condition based on sideslips of a number of autonomous vehicles of the type under identical driving conditions; and

storing a driving scenario to a sideslip (scenario/sideslip) mapping table having a plurality of mapping entries to map the driving scenario to the calculated average sideslip, wherein the scenario/sideslip mapping table is utilized to compensate a sideslip under a similar driving scenario, the operations further including

determining a driving scenario based on a driving condition of an autonomous vehicle;

identifying an entry matching the driving scenario from the scenario/sideslip mapping table;

obtaining an average sideslip from the matching entry; and

adjusting a steering angle of the autonomous vehicle using the obtained average sideslip.

9. The machine-readable medium of claim 8, wherein the operations further comprise:

in response to determining a next movement of the autonomous vehicle, determining a first driving scenario corresponding to the next movement;

searching in the scenario/sideslip mapping table based on the first driving scenario to locate a mapping entry corresponding to a second driving scenario matching the first driving scenario; and

determining the next movement for the autonomous vehicle by taking into consideration of a first sideslip obtained from the located mapping entry.

10. The machine-readable medium of claim 9, wherein determining the next movement for the autonomous vehicle by taking into consideration of a first sideslip obtained from the located mapping entry comprises:

calculating a first steering angle based on a speed required by a route associated with the next movement using a predetermined algorithm;

modifying the first steering angle in view of the first sideslip obtained from the located mapping entry, generating a second steering angle; and

controlling the autonomous vehicle based on the second steering angle.

11. The machine-readable medium of claim 8, wherein the driving condition comprises at least one of a type of road or a weather condition.

12. The machine-readable medium of claim 8, wherein a driving parameter identifies an action an autonomous vehicle is taking, a speed during the action, or a steering angle during the action.

13. The machine-readable medium of claim 12, wherein the driving parameter further identifies a type of an autonomous vehicle involved.

14. The machine-readable medium of claim 12, wherein the action is one of a left turn, a right turn, or a U-turn.

15. A data processing system, comprising:

a processor; and

a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations of operating an autonomous vehicle, the operations including

defining a plurality of driving scenarios for a plurality of types of autonomous vehicles, each driving scenario corresponding to a specific movement of a particular type of autonomous vehicles,

for each of the driving scenarios of each type of autonomous vehicles, obtaining driving statistics of each autonomous vehicle, including

recording one or more driving parameters used to control and drive each autonomous vehicle of the type,

capturing a driving condition corresponding the driving scenario, and

calculating an average sideslip caused by the driving parameters and the driving condition based on sideslips of a number of autonomous vehicles of the type under identical driving conditions, and

storing a driving scenario to a sideslip (scenario/sideslip) mapping table having a plurality of mapping entries to map the driving scenario to the calculated average sideslip, wherein the scenario/sideslip mapping table is utilized to compensate a sideslip under a similar driving scenario, the operations further including

determining a driving scenario based on a driving condition of an autonomous vehicle;

identifying an entry matching the driving scenario from the scenario/sideslip mapping table;

obtaining an average sideslip from the matching entry; and

adjusting a steering angle of the autonomous vehicle using the obtained average sideslip.

16. The system of claim 15, wherein the operations further comprise:

in response to determining a next movement of the autonomous vehicle, determining a first driving scenario corresponding to the next movement;

searching in the scenario/sideslip mapping table based on the first driving scenario to locate a mapping entry corresponding to a second driving scenario matching the first driving scenario; and

determining the next movement for the autonomous vehicle by taking into consideration of a first sideslip obtained from the located mapping entry.

17. The system of claim 16, wherein determining the next movement for the autonomous vehicle by taking into consideration of a first sideslip obtained from the located mapping entry comprises:

calculating a first steering angle based on a speed required by a route associated with the next movement using a predetermined algorithm;

modifying the first steering angle in view of the first sideslip obtained from the located mapping entry, generating a second steering angle; and

controlling the autonomous vehicle based on the second steering angle.

18. The system of claim 15, wherein the driving condition comprises at least one of a type of road or a weather condition.

19. The system of claim 15, wherein a driving parameter identifies an action an autonomous vehicle is taking, a speed during the action, or a steering angle during the action.

20. The system of claim 19, wherein the driving parameter further identifies a type of an autonomous vehicle involved.

21. The system of claim 19, wherein the action is one of a left turn, a right turn, or a U-turn.