Gm Cruise Holdings LLC

Toyota Jidosha Kabushiki Kaisha

Toyota Research Institute, Inc.

Sachithra Hemachandra

Shuai Zheng

Jacopo Serafin

Jonathan A Daudelin

Ajaya Hanasoge Shankaranarayana Rao

Amirreza Shaban

Navaneeth Bodla

Gia Tri Nguyen

Xiao Zhang

Machine learning models for controlling AV operations may be evaluated based on sliced data. A feature category critical for an environmental condition may be identified. The environmental condition is a condition in an environment where an AV performs an operation. A sub-dataset, which comprises sensor data capturing the environmental feature dataset, may be extracted from a dataset that comprises sensor data capturing the environment. The sub-dataset may be input into machine learning models that can classify the feature category. Performances of the machine learning models can be evaluated based on their outputs, which are generated based on the sub-dataset, and a ground-truth classification of the environmental feature. The output of each machine learning model comprises a classification of the environmental feature. A machine learning model may be selected based on the evaluated performances of the machine learning models and may be used to control driverless operations of AVs.

Using sliced data to evaluate machine learning models used for autonomous vehicle operation

Autonomous vehicles utilize perception and understanding of road users to predict behaviors of the road users, and to plan a trajectory for the vehicle. Understanding subtypes and attributes of vehicles may help autonomous vehicles better predict behaviors of and react to vehicles in a variety of road situations. To offer additional understanding capabilities, an additional understanding model is added to the perception and understanding pipeline to improve classification of vehicles and extraction of attributes of the vehicles. The exemplary architectures of the understanding model balance recall and precision performance metrics and computational complexity.

Perception and understanding of vehicles

Systems, methods, and other embodiments described herein relate to detecting reflection artifacts in a point cloud. In one embodiment, a method includes generating a first convex hull for defining an outline of a first cluster of points in a depth image and generating a second convex hull for defining an outline of a second cluster of points in the depth image. The first cluster of points originates from a point cloud and the second cluster of points originates from the point cloud. The method includes determining whether the second cluster of points is a reflection artifact based on identifying if a portion of the second convex hull is enclosed in the first convex hull and a range of the second convex hull is larger than a range of the first convex hull.

Systems and methods for detecting a reflection artifact in a point cloud

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Sunnyvale, CA, United States of America

Sachithra Hemachandra