Dangerous road user detection and response
AssigneeGM Cruise Holdings LLC
InventorsTravis McCawley, Christopher Ward, Saranya Konala, Nikola Noxon
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Abstract
Methods and systems are provided for detecting and responding to dangerous road users. In some aspects, a process can include steps for receiving sensor data of a detected object from an autonomous vehicle, determining whether the detected object is exhibiting a dangerous attribute, generating output data based on the determining of whether the detected object is exhibiting the dangerous attribute, and updating a machine learning model based on the output data relating to the dangerous attribute.
Claims20
A computer-implemented method for detecting and responding to dangerous road users, the computer-implemented method comprising:
- receiving, at an autonomous vehicle system, sensor data of a detected object from an autonomous vehicle;
- generating, by the autonomous vehicle system, a three-dimensional (3D) bounding box around the detected object, wherein the 3D bounding box includes attributes, which comprise a dimension, a position, an orientation, a classification of the detected object, and a confidence of the 3D bounding box;
- determining, by the autonomous vehicle system, whether the detected object is exhibiting a dangerous attribute based on a behavior of the detected object and the attributes of the 3D bounding box;
- generating, by the autonomous vehicle system, output data based on the determining of whether the detected object is exhibiting the dangerous attribute;
- and
- updating, by the autonomous vehicle system, a machine learning model based on the output data relating to the dangerous attribute.
A system for detecting and responding to dangerous road users, the system comprising:
- one or more processors;
- and
- at least one non-transitory computer-readable storage medium having stored therein instructions which, when executed by the one or more processors, cause the system to:
- receive sensor data of a detected object from an autonomous vehicle;
- generate a three-dimensional (3D) bounding box around the detected object, wherein the 3D bounding box includes attributes, which comprise a dimension, a position, an orientation, a classification of the detected object, and a confidence of the 3D bounding box;
- determine whether the detected object is exhibiting a dangerous attribute based on a behavior of the detected object and the attributes of the 3D bounding box;
- generate output data based on the determining of whether the detected object is exhibiting the dangerous attribute;
- and
- update a machine learning model based on the output data relating to the dangerous attribute.
A non-transitory computer-readable storage medium comprising:
- instructions stored on the non-transitory computer-readable storage medium, the instructions, when executed by one more processors, cause the one or more processors to:
- receive sensor data of a detected object from an autonomous vehicle;
- generate a three-dimensional (3D) bounding box around the detected object, wherein the 3D bounding box includes attributes, which comprise a dimension, a position, an orientation, a classification of the detected object, and a confidence of the 3D bounding box;
- determine whether the detected object is exhibiting a dangerous attribute based on a behavior of the detected object and the attributes of the 3D bounding box;
- generate output data based on the determining of whether the detected object is exhibiting the dangerous attribute;
- and
- update a machine learning model based on the output data relating to the dangerous attribute.
Dependencies are inferred from the claim text.