公开(公告)号：US20250003764A1

公开(公告)日：2025-01-02

申请号：US18342478

申请日：2023-06-27

Applicant: TORC Robotics, Inc.

Inventor： Harish PULLAGURLA ,  Ryan CHILTON ,  Harish KARUNAKARAN

IPC: G01C21/00 ,  B60W40/06

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.

公开(公告)号：US20240426632A1

公开(公告)日：2024-12-26

申请号：US18341469

申请日：2023-06-26

Applicant: TORC Robotics, Inc.

Inventor： Harish PULLAGURLA ,  Ryan CHILTON ,  Jason HARPER ,  Jordan STONE

IPC: G01C21/00

Abstract: Systems and methods of automatic correction of map data for autonomous vehicle navigation are disclosed. One or more servers can receive, from a first autonomous vehicle traveling on a road, a first correction to map data identifying a location in the map data; generate a modified parameter of the map data based on the first correction and a second correction identifying the location, the second correction received from a second autonomous vehicle traveling on the road; update the map data based on the modified parameter; and provide the updated map data to the first autonomous vehicle.

公开(公告)号：US20240101147A1

公开(公告)日：2024-03-28

申请号：US18303460

申请日：2023-04-19

Applicant: TORC Robotics, Inc.

Inventor： Ryan CHILTON ,  Harish PULLAGURLA ,  Joseph STAMENKOVICH

IPC: B60W60/00 ,  G06V10/774 ,  G06V20/56

CPC classification number: B60W60/001 ,  G06V10/774 ,  G06V20/588 ,  B60W2556/40

Abstract: Systems and methods for training and executing machine learning models to generate lane index values are disclosed. A method includes identifying a set of image data captured by at least one autonomous vehicle when the at least autonomous vehicle is positioned in a lane of a roadway and respective ground truth localization data; determining a plurality of lane index values for the set of image data based on the ground truth localization data; labeling the set of image data with the plurality of lane index values, the lane index values representing a number of lanes from a leftmost or rightmost lane to the lane in which the at least one autonomous vehicle was positioned; and training, using the labeled set of image data, a plurality of machine learning models that generate a left lane index value and a right lane index value as output.