View source for GM Cruise Holdings LLC (20240290143). SYSTEMS AND TECHNIQUES FOR APPLYING SCENE SELECTORS TO ROAD DATA AND SIMULATION DATA FOR CHARACTERIZING AUTONOMOUS VEHICLE PERFORMANCE simplified abstract

=SYSTEMS AND TECHNIQUES FOR APPLYING SCENE SELECTORS TO ROAD DATA AND SIMULATION DATA FOR CHARACTERIZING AUTONOMOUS VEHICLE PERFORMANCE=


==Organization Name==

[[:Category:GM Cruise Holdings LLC|GM Cruise Holdings LLC]]


[[Category:GM Cruise Holdings LLC]]

==Inventor(s)==

[[:Category:Nicholas Famiglietti of Torrance CA (US)|Nicholas Famiglietti of Torrance CA (US)]][[Category:Nicholas Famiglietti of Torrance CA (US)]]

[[:Category:Katelyn Wolfenberger of San Francisco CA (US)|Katelyn Wolfenberger of San Francisco CA (US)]][[Category:Katelyn Wolfenberger of San Francisco CA (US)]]

[[:Category:Pei Xu of Sherman Oaks CA (US)|Pei Xu of Sherman Oaks CA (US)]][[Category:Pei Xu of Sherman Oaks CA (US)]]

[[:Category:Changkai Zhou of Mountain View CA (US)|Changkai Zhou of Mountain View CA (US)]][[Category:Changkai Zhou of Mountain View CA (US)]]

[[:Category:Nestor Grace of San Francisco CA (US)|Nestor Grace of San Francisco CA (US)]][[Category:Nestor Grace of San Francisco CA (US)]]

[[:Category:Kevin Yuwen Tong of Carlsbad CA (US)|Kevin Yuwen Tong of Carlsbad CA (US)]][[Category:Kevin Yuwen Tong of Carlsbad CA (US)]]

==SYSTEMS AND TECHNIQUES FOR APPLYING SCENE SELECTORS TO ROAD DATA AND SIMULATION DATA FOR CHARACTERIZING AUTONOMOUS VEHICLE PERFORMANCE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240290143 titled 'SYSTEMS AND TECHNIQUES FOR APPLYING SCENE SELECTORS TO ROAD DATA AND SIMULATION DATA FOR CHARACTERIZING AUTONOMOUS VEHICLE PERFORMANCE

'''Simplified Explanation:'''
The patent application discusses systems and techniques for detecting traffic scenes and evaluating the performance of autonomous vehicles in those scenes.

* Autonomous vehicles collect data while navigating real-world environments.
* Traffic scene datasets are identified based on the collected data.
* Metrics are determined to characterize the operation of autonomous vehicles in relation to traffic scenes.

'''Key Features and Innovation:'''
* Detection of traffic scenes and evaluation of autonomous vehicle performance.
* Utilization of collected data to identify traffic scene datasets.
* Generation of metrics to assess autonomous vehicle operation in traffic scenes.

'''Potential Applications:'''
This technology can be applied in the development and testing of autonomous vehicles, improving their performance and safety in various traffic scenarios.

'''Problems Solved:'''
* Enhancing the understanding of autonomous vehicle behavior in traffic scenes.
* Providing valuable insights for optimizing autonomous vehicle software and systems.

'''Benefits:'''
* Improved safety and efficiency of autonomous vehicles in real-world traffic conditions.
* Enhanced development and testing processes for autonomous vehicle technology.

'''Commercial Applications:'''
The technology can be utilized by autonomous vehicle manufacturers, transportation companies, and research institutions to advance the capabilities of autonomous driving systems and ensure their reliability on the road.

'''Questions about Traffic Scene Detection and Autonomous Vehicle Performance:'''
1. How does the technology in the patent application contribute to the advancement of autonomous vehicle technology?
2. What are the potential implications of using traffic scene data to evaluate autonomous vehicle performance?


==Original Abstract Submitted==

systems and techniques are provided for detecting occurrence of traffic scenes and characterizing performance of autonomous vehicles (avs) in relation to said traffic scenes. an example method includes receiving a collection of data compiled by one or more avs while navigating a real-world environment, wherein the one or more avs are configured to execute a first version of av software; identifying, based on at least one traffic scene selector and the collection of data, a plurality of traffic scene datasets each corresponding to an instance in which the one or more avs encountered a traffic scene; and determining, based on the plurality of traffic scene datasets, one or more metrics for characterizing an operation of the one or more avs in relation to the traffic scene.



[[Category:G07C5/08]]
[[Category:G06F30/20]]
[[Category:CPC_G07C5/0808]]