Dynamic model with learning based localization correction system

Patent metadata

Field	Value
Publication number	US-11269329-B2
Application number	US-201916659040-A
Country	US
Kind code	B2
Filing date	Oct 21, 2019
Priority date	Oct 21, 2019
Publication date	Mar 8, 2022
Grant date	Mar 8, 2022

Abstract

In one embodiment, a set of parameters representing a first state of an autonomous driving vehicle (ADV) to be simulated and a set of control commands to be issued at a first point in time. In response, a localization predictive model is applied to the set of parameters to determine a first position (e.g., x, y) of the ADV. A localization correction model is applied to the set of parameters to determine a set of localization correction factors (e.g., Δx, Δy). The correction factors may represent the errors between the predicted position of the ADV by the localization predictive model and the ground truth measured by sensors of the vehicle. Based on the first position of the ADV and the correction factors, a second position of the ADV is determined as the simulated position of the ADV.

First claim

What is claimed is:

1. A computer-implemented method for simulating an autonomous driving vehicle, the method comprising:
receiving a set of parameters representing a first state of an autonomous driving vehicle (ADV) to be simulated and a set of control commands to be issued at a first point in time;
applying a localization predictive model to the set of parameters to determine a first position of the ADV;
applying a localization correction model to the set of parameters to determine a set of localization correction factors; and
determining a second position of the ADV based on the first position and the localization correction factors, wherein the second position is utilized as a simulated position of the ADV at a second point in time.

2. The method of claim 1 , wherein the first state of the ADV includes a speed, an acceleration, and an angular velocity of the ADV at the first point in time.

3. The method of claim 1 , wherein the set of control commands comprises at least one of a throttle command, a brake command, or a steering command.

4. The method of claim 1 , wherein the localization correction model was trained based on a large amount of driving statistics data collected from a plurality of vehicles.

5. The method of claim 4 , wherein the driving statistics data comprises vehicle states of the vehicles in response to different control commands issued at different points in time over a period of time.

6. The method of claim 4 , wherein the localization correction model is to determine a difference between an expected position of a vehicle based on a localization process of an autonomous driving system and an actual position of the vehicle determined based on sensor data obtained from a plurality of sensors mounted on the vehicle.

7. The method of claim 6 , wherein the difference between the expected position and the actual position is utilized to calibrate the first position determined by the localization predictive model.

8. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations, the operations comprising:
receiving a set of parameters representing a first state of an autonomous driving vehicle (ADV) to be simulated and a set of control commands to be issued at a first point in time;
applying a localization predictive model to the set of parameters to determine a first position of the ADV;
applying a localization correction model to the set of parameters to determine a set of localization correction factors; and
determining a second position of the ADV based on the first position and the localization correction factors, wherein the second position is utilized as a simulated position of the ADV at a second point in time.

9. The machine-readable medium of claim 8 , wherein the first state of the ADV includes a speed, an acceleration, and an angular velocity of the ADV at the first point in time.

10. The machine-readable medium of claim 8 , wherein the set of control commands comprises at least one of a throttle command, a brake command, or a steering command.

11. The machine-readable medium of claim 8 , wherein the localization correction model was trained based on a large amount of driving statistics data collected from a plurality of vehicles.

12. The machine-readable medium of claim 11 , wherein the driving statistics data comprises vehicle states of the vehicles in response to different control commands issued at different points in time over a period of time.

13. The machine-readable medium of claim 11 , wherein the localization correction model is to determine a difference between an expected position of a vehicle based on a localization process of an autonomous driving system and an actual position of the vehicle determined based on sensor data obtained from a plurality of sensors mounted on the vehicle.

14. The machine-readable medium of claim 13 , wherein the difference between the expected position and the actual position is utilized to calibrate the first position determined by the localization predictive model.

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, the operations including:
receiving a set of parameters representing a first state of an autonomous driving vehicle (ADV) to be simulated and a set of control commands to be issued at a first point in time,
applying a localization predictive model to the set of parameters to determine a first position of the ADV,
applying a localization correction model to the set of parameters to determine a set of localization correction factors, and
determining a second position of the ADV based on the first position and the localization correction factors, wherein the second position is utilized as a simulated position of the ADV at a second point in time.

16. The system of claim 15 , wherein the first state of the ADV includes a speed, an acceleration, and an angular velocity of the ADV at the first point in time.

17. The system of claim 15 , wherein the set of control commands comprises at least one of a throttle command, a brake command, or a steering command.

18. The system of claim 15 , wherein the localization correction model was trained based on a large amount of driving statistics data collected from a plurality of vehicles.

19. The system of claim 18 , wherein the driving statistics data comprises vehicle states of the vehicles in response to different control commands issued at different points in time over a period of time.

20. The system of claim 18 , wherein the localization correction model is to determine a difference between an expected position of a vehicle based on a localization process of an autonomous driving system and an actual position of the vehicle determined based on sensor data obtained from a plurality of sensors mounted on the vehicle.

Assignees

• Baidu Usa Llc

Inventors

• Jiang Shu
• Luo Qi
• Miao Jinghao
• Hu Jiangtao
• Xu Jiaxuan
• Wang Jingao
• Wang Yu
• Zhou Jinyun
• He Runxin

Classifications

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06F11/3698

  Environments for analysis, debugging or testing of software · CPC title

• G08G1/096827

  where the route is computed onboard · CPC title

• G08G1/052

  with provision for determining speed or overspeed {(speed measuring in general G01P)} · CPC title