Lyft, Inc.

Woven by Toyota, U.s., Inc.

Alan Agon

Sammy Omari

Sameer Qureshi

Mason Lee

Luc Vincent

Michael Jared Benisch

Nastaran Ghadar

Meng Tao

Yunjian Jiang

Muhammad Usman

Ana Sofia Rufino Ferreira

Moritz Niendorf

Carey Stover Nachenberg

Carlos Alberto De Magalhaes Massera Filho

Examples disclosed herein may involve a computing system that is operable to (i) identify, within a given period of operation of a vehicle having an associated sensor system for capturing sensor data, one or more times when the vehicle was driving in a lane having substantially-straight lane geometry, (ii) for each identified time, determine a respective measure of a lateral offset between the vehicle's associated sensor system and a lateral reference point of the vehicle, and (iii) based on the respective measure of the lateral offset that is determined for each of the one or more identified times, determine the lateral offset between the vehicle's associated sensor system and the lateral reference point of the vehicle.

Detecting positioning of a sensor system associated with a vehicle

Systems, methods, and non-transitory computer-readable media can detect an occurrence of a condition in an environment based on sensor data captured by a vehicle. A determination is made whether the occurrence of the condition satisfies a threshold associated with a likelihood that a behavior associated with an object in the environment will occur based on an interaction between the condition and the object, wherein the likelihood is based on prior observations of one or more objects. Subsequent to determining that the threshold is satisfied, a vehicle operation that is associated with the likelihood that the behavior associated with the object will occur is performed.

Systems and methods for configuring autonomous vehicle operation

A method includes generating a parameter of a trajectory associated with a scenario using a path planner. The parameter is generated based on a training dataset. The method includes comparing the parameter of the trajectory against a validation parameter associated with a validation dataset. The validation parameter is based on human-based vehicle driving trajectory data associated with scenarios that satisfy a level of similarity with the scenario. The method further includes determining a level of similarity between the parameter associated with the scenario and the validation parameter associated with the scenarios, and, subsequent to determining that the level of similarity fails to satisfy a similarity threshold, the method concludes with providing training data associated with the scenario to the training dataset so that a subsequent parameter of a subsequent trajectory generated by the path planner and associated with the scenario satisfies the level of similarity against the validation parameter.

Active learning and validation system for vehicles

Growing community of inventors

Sunnyvale, CA, United States of America

Alan Agon