Gm Cruise Holdings LLC

Abdelrahman Elogeel

Sandeep Gangundi

Marzieh Parandehgheibi

Teng Liu

Debanjan Nandi

Alexander Pon

Carden Bagwell

Andres Hasfura

Kyle Vogt

Cornelis Jacob Lekkerkerker

Avinash Nair

Zachary Witte

The present technology is directed to identifying and labeling a vehicle appendage. More specifically, the present technology is generally related to receiving one or more lidar points associated with a vehicle having an appendage and outputting a label to classify the vehicle and the appendage. In some examples, a first portion of the one or more lidar points associated with the vehicle having the appendage represents the vehicle and a second portion of the one or more lidar points associated with the vehicle having the appendage represents the appendage The present disclosure can further train a perception model to output the label to classify the vehicle and the appendage.

Labeling system for a vehicle appendage

The subject disclosure relates to techniques for improving performance of a machine learning algorithm that at least receives data descriptive of a 3-D object and provides an output, where the 3-D object occurs infrequently in a training dataset. A process of the disclosed technology can include determining that the machine learning algorithm performed below a threshold performance score when receiving data descriptive of the 3-D object in a real-world scene, wherein the 3-D object is classified as a first type of object, creating at least one 3-D representation of the first type of object for use in a simulation, modifying a plurality of simulated scenes to include the at least one 3-D representation of the first type of object, and training the machine learning algorithm with the modified simulated scenes, whereby the machine learning algorithm has greater exposure to the first type of object.

Long tail lidar 3-D object detection improvement with targeted simulation data injection

The subject disclosure relates to techniques for managing the configuration of vehicles. A process of the disclosed technology can include steps of receiving a first vehicle identifier for a first vehicle, wherein the first vehicle is associated with a first vehicle platform, identifying a first configuration for the first vehicle platform based on the first vehicle identifier, receiving a second vehicle identifier for a second vehicle, wherein the second vehicle is associated with a second vehicle platform, and wherein the first vehicle platform is different from the second vehicle platform, and identifying a second configuration for the second vehicle platform based on the second vehicle identifier, wherein the first vehicle and the second vehicle run on an identical software stack. Systems and machine-readable media are also provided.

Configuration management system for autonomous vehicle software stack

The subject disclosure relates to ways to identify self-hit data collected by autonomous vehicle (AV) sensors. In some aspects, a method of the disclosed technology includes steps for generating a geometric model of an autonomous vehicle (AV), wherein the geometric model specifies physical boundaries of the AV in three-dimensional (3D) space, collecting sensor data for an environment around the AV, and identifying one or more data points, from among the collected sensor data, that correspond with a surface of the AV. Systems and machine-readable media are also provided.

Eliminating sensor self-hit data

Growing community of inventors

Sunnyvale, CA, United States of America

Abdelrahman Elogeel