World model generation and correction for autonomous vehicles

Patent metadata

Field	Value
Publication number	US-12498252-B2
Application number	US-202318342400-A
Country	US
Kind code	B2
Filing date	Jun 27, 2023
Priority date	Jun 27, 2023
Publication date	Dec 16, 2025
Grant date	Dec 16, 2025

Abstract

Systems and methods of generating and updating a world model for autonomous vehicle navigation are disclosed. An autonomous vehicle system can receive sensor data from a plurality of sensors of an autonomous vehicle, where the sensor data is captured during operation of the autonomous vehicle; access a world model generated based at least on map information corresponding to a location of the operation of the autonomous vehicle; determine at least one semantic correction for the world model based on the sensor data; determine at least one geometric correction for the world model based on the sensor data and the map information; and generate an updated world model based on the at least one semantic correction and the at least one geometric correction.

First claim

What is claimed is:

1 . A system, comprising:
at least one processor coupled to non-transitory memory, the at least one processor configured to:
retrieve, from a world model, expected semantic data for a road traveled by an autonomous vehicle;
receive sensor data from a plurality of sensors of the autonomous vehicle, the sensor data captured during operation of the autonomous vehicle;
detect semantic attributes of the road based on the sensor data;
detect an error in the expected semantic data based on the sensor data by:
comparing the detected semantic attributes with corresponding semantic attributes of the road in the expected semantic data,
wherein the error indicates a semantic error in the world model that mismatches an attribute of the road;

generate a correction to the world model based on the error;
modify the world model based on the correction; and
navigate the autonomous vehicle based at least in part on the modified world model.

2 . The system of claim 1 , wherein the at least one processor is further configured to modify a speed limit identified in the world model based on the correction.

3 . The system of claim 1 , wherein the plurality of sensors comprises one or more of a light detection and ranging (LiDAR) sensor, a radar sensor, a camera, or an inertial measurement unit (IMU).

4 . The system of claim 1 , wherein the expected semantic data comprises one or more of a speed limit for the road, a lane type of a lane of the road, a presence of a road sign corresponding of the road, or a type of the road sign.

5 . The system of claim 1 , wherein the at least one processor is further configured to transmit the correction to at least one server to correct corresponding map information.

6 . The system of claim 1 , wherein the at least one processor is further configured to:
detect, based on the sensor data, one or more objects corresponding to the road traveled by the autonomous vehicle; and generate additional semantic data for the road based on a classification of the one or more objects.

7 . The system of claim 6 , wherein the at least one processor is further configured to generate the correction based on the additional semantic data.

8 . A method, comprising:
retrieving, by at least one processor coupled to non-transitory memory, from a world model, expected semantic data for a road traveled by an autonomous vehicle; receiving, by the at least one processor, sensor data from a plurality of sensors of the autonomous vehicle, the sensor data captured during operation of the autonomous vehicle; detecting semantic attributes of the road based on the sensor data; detecting, by the at least one processor, an error in the expected semantic data based on the sensor data by:
comparing the detected semantic attributes with corresponding semantic attributes of the road in the expected semantic data,
wherein the error indicates a semantic error in the world model that mismatches an attribute of the road;
generating a correction to the world model based on the error; modifying the world model based on the correction; and navigating the autonomous vehicle based at least in part on the modified world model.

9 . The method of claim 8 , further comprising modifying, by the at least one processor, a speed limit identified in the world model based on the correction.

10 . The method of claim 8 , wherein the plurality of sensors comprises one or more of a light detection and ranging (LiDAR) sensor, a radar sensor, a camera, or an inertial measurement unit (IMU).

11 . The method of claim 8 , wherein the expected semantic data comprises one or more of a speed limit for the road, a lane type of a lane of the road, a presence of a road sign corresponding of the road, or a type of the road sign.

12 . The method of claim 8 , further comprising transmitting, by the at least one processor, the correction to at least one server to correct corresponding map information.

13 . The method of claim 8 , further comprising:
detecting, by the at least one processor, based on the sensor data, one or more objects corresponding to the road traveled by the autonomous vehicle; and generating, by the at least one processor, additional semantic data for the road based on a classification of the one or more objects.

14 . The method of claim 13 , further comprising generating, by the at least one processor, the correction based on a comparison of the additional semantic data and the expected semantic data retrieved from the world model.

15 . An autonomous vehicle, comprising:
a plurality of sensors; and at least one processor coupled to non-transitory memory, the at least one processor configured to:
receive, during operation of the autonomous vehicle, sensor data from the plurality of sensors;
detect semantic attributes of a road based on the sensor data;
detect, based on the sensor data, an error in expected semantic data of a world model used in navigation of the autonomous vehicle by:
comparing the detected semantic attributes with corresponding semantic attributes of the road in the expected semantic data,
wherein the error indicates a semantic error in the world model that mismatches an attribute of the road;

generate an updated world model based on the error; and
navigate the autonomous vehicle based at least in part on the updated world model.

16 . The autonomous vehicle of claim 15 , wherein the plurality of sensors comprises one or more of a light detection and ranging (LiDAR) sensor, a radar sensor, a camera, or an inertial measurement unit (IMU).

17 . The autonomous vehicle of claim 15 , wherein the at least one processor is further configured to:
detect, based on the sensor data, one or more objects corresponding to a road traveled by the autonomous vehicle; and generate additional semantic data for the road based on a classification of the one or more objects.

18 . The autonomous vehicle of claim 17 , wherein the at least one processor is further configured to detect the error based on a mismatch between the expected semantic data of the world model and the additional semantic data.

Assignees

• Torc Robotics Inc

Inventors

• Pullagurla Harish
• Chilton Ryan
• Karunakaran Harish

Classifications

• B60W2420/408

  Radar; Laser, e.g. lidar · CPC title

• B60W2420/403

  Image sensing, e.g. optical camera · CPC title

• B60W60/001

  Planning or execution of driving tasks · CPC title

• B60W2555/60

  Traffic rules, e.g. speed limits or right of way · CPC title

• B60W2520/10

  Longitudinal speed · CPC title