Physical model and machine learning combined method to simulate autonomous vehicle movement

• US 10,343,685 B2
• Filed: 09/28/2016
• Issued: 07/09/2019
• Est. Priority Date: 09/28/2016
• Status: Active Grant

• Current Assignee: Baidu USA LLC (Baidu Incorporated)
• Original Assignee: Baidu USA LLC (Baidu Incorporated)
• Inventors: Zhu, Fan, Kong, Qi, Yang, Guang, Wang, Jingao
• Primary Examiner(s): Merlino, David P.
• Application Number:
  US15/278,719
• Publication Number:
  US 20180086344A1
• Time in Patent Office: 1,014 Days
• Field of Search: None
• 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 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2520/105 Longitudinal acceleration
• CPC Class Codes: B60W 2520/20 Sideslip angle
• CPC Class Codes: B60W 2552/05 Type of road, e.g. motorways, local streets, paved or unpaved roads
• 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 30/18145 Cornering
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: G05D 1/0212 with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287

Abstract

In one embodiment, a driving scenario is identified for a next movement for an autonomous vehicle, where the driving scenario is represented by a set of one or more predetermined parameters. A first next movement is calculated for the autonomous vehicle using a physical model corresponding to the driving scenario. A sideslip predictive model is applied to the set of predetermined parameters to predict a sideslip of the autonomous vehicle under the driving scenario. A second next movement of the autonomous vehicle is determined based on the first next movement and the predicted sideslip of the autonomous vehicle. The predicted sideslip is utilized to modify the first next movement to compensate the sideslip. Planning and control data is generated for the second next movement and the autonomous vehicle is controlled and driven based on the planning and control data.

Claims

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

identifying a driving scenario of the autonomous vehicle for a next movement, wherein the driving scenario is represented by a set of one or more predetermined parameters;

calculating a first next movement of the autonomous vehicle under the driving scenario using a physical model;

generating planning and control data based on the first next movement, the planning and control data describing a path to drive the autonomous vehicle from a first point to a second point;

applying a sideslip predictive model to the set of predetermined parameters to determine a sideslip of the autonomous vehicle given a set of driving conditions, the sideslip representing a difference between a first position of the autonomous vehicle and a second position of the autonomous vehicle, wherein the first position is an expected position of the autonomous vehicle calculated using the physical model and the set of predetermined parameters, and wherein the second position is a position where the autonomous vehicle actually ends up due to the set of driving conditions;

determining a second next movement of the autonomous vehicle based on the first next movement and the determined sideslip of the autonomous vehicle, including adjusting at least one position within the path between the first point and the second point based on the determined sideslip of the autonomous vehicle;

modifying the planning and control data based on the second next movement; and

controlling and driving the autonomous vehicle from the first point to the second point according to the path based on the modified planning and control data.

2. The method of claim 1, wherein the sideslip predictive model is one of a plurality of sideslip predictive models that were generated using machine learning based on sideslips and corresponding driving parameters measured and recorded under a plurality of driving scenarios by a plurality of different types of vehicles.

3. The method of claim 1, wherein determining the second next movement comprises calculating a final position of the autonomous vehicle by adding an original position, a movement of wheels, and the determined sideslip.

4. The method of claim 1, wherein the determined sideslip is used to correct the first next movement of the autonomous vehicle calculated based on the physical model.

5. The method of claim 1, wherein the driving scenario includes a scenario of turning of the autonomous vehicle.

6. The method of claim 1, wherein the predetermined parameters comprise one or more of a road type, a weather condition, a speed of the autonomous vehicle, a target angle of the autonomous vehicle, an acceleration or deceleration of the autonomous vehicle, and a steering angle of the autonomous vehicle.

7. 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:

identifying a driving scenario of the autonomous vehicle for a next movement, wherein the driving scenario is represented by a set of one or more predetermined parameters;

calculating a first next movement of the autonomous vehicle under the driving scenario using a physical model;

generating planning and control data based on the first next movement, the planning and control data describing a path to drive the autonomous vehicle from a first point to a second point;

applying a sideslip predictive model to the set of predetermined parameters to determine a sideslip of the autonomous vehicle given a set of driving conditions, the sideslip representing a difference between a first position of the autonomous vehicle and a second position of the autonomous vehicle, wherein the first position is an expected position of the autonomous vehicle calculated using the physical model and the sot of predetermined parameters, and wherein the second position is a position where the autonomous vehicle actually ends UP due to the set of driving conditions;

determining a second next movement of the autonomous vehicle based on the first next movement and the determined sideslip of the autonomous vehicle, including adjusting at least one position within the path between the first point and the second point based on the determined sideslip of the autonomous vehicle;

modifying the planning and control data based on the second next movement; and

controlling and driving the autonomous vehicle from the first point to the second point according to the path based on the modified planning and control data.

8. The machine-readable medium of claim 7, wherein the sideslip predictive model is one of a plurality of sideslip predictive models that were generated using machine learning based on sideslips and corresponding driving parameters measured and recorded under a plurality of driving scenarios by a plurality of different types of vehicles.

9. The machine-readable medium of claim 7, wherein determining the second next movement comprises calculating a final position of the autonomous vehicle by adding an original position, a movement of wheels, and the determined sideslip.

10. The machine-readable medium of claim 7, wherein the determined sideslip is used to correct the first next movement of the autonomous vehicle calculated based on the physical model.

11. The machine-readable medium of claim 7, wherein the driving scenario includes a scenario of turning of the autonomous vehicle.

12. The machine-readable medium of claim 7, wherein the predetermined parameters comprise one or more of a road type, a weather condition, a speed of the autonomous vehicle, a target angle of the autonomous vehicle, an acceleration or deceleration of the autonomous vehicle, and a steering angle of the autonomous vehicle.

13. 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

identifying a driving scenario of the autonomous vehicle for a next movement, wherein the driving scenario is represented by a set of one or more predetermined parameters,

calculating a first next movement of the autonomous vehicle under the driving scenario using a physical model,

generating planning and control data based on the first next movement, the planning and control data describing a path to drive the autonomous vehicle from a first point to a second point,

applying a sideslip predictive model to the set of predetermined parameters to determine a sideslip of the autonomous vehicle given a set of driving conditions, the sideslip representing a difference between a first position of the autonomous vehicle and a second position of the autonomous vehicle, wherein the first position is an expected position of the autonomous vehicle calculated using the physical model and the set of predetermined parameters, and wherein the second position is a position where the autonomous vehicle actually ends up due to the set of driving conditions;

determining a second next movement of the autonomous vehicle based on the first next movement and the determined sideslip of the autonomous vehicle, including adjusting at least one position within the path between the first point and the second point based on the determined sideslip of the autonomous vehicle,

modifying the planning and control data based on the second next movement, and

controlling and driving the autonomous vehicle from the first point to the second point according to the path based on the modified planning and control data.

14. The system of claim 13, wherein the sideslip predictive model is one of a plurality of sideslip predictive models that were generated using machine learning based on sideslips and corresponding driving parameters measured and recorded under a plurality of driving scenarios by a plurality of different types of vehicles.

15. The system of claim 13, wherein determining the second next movement comprises calculating a final position of the autonomous vehicle by adding an original position, a movement of wheels, and the determined sideslip.

16. The system of claim 13, wherein the determined sideslip is used to correct the first next movement of the autonomous vehicle calculated based on the physical model.

17. The system of claim 13, wherein the driving scenario includes a scenario of turning of the autonomous vehicle.

18. The system of claim 13, wherein the predetermined parameters comprise one or more of a road type, a weather condition, a speed of the autonomous vehicle, a target angle of the autonomous vehicle, an acceleration or deceleration of the autonomous vehicle, and a steering angle of the autonomous vehicle.