Generating Safety-Critical Driving Scenarios for the Design of the CAV Proving-Ground – Using Domain Knowledge, Causality, and Large Language Models

Zhao, Ding; Mobility21, Carnegie Mellon University

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Generating Safety-Critical Driving Scenarios for the Design of the CAV Proving-Ground – Using Domain Knowledge, Causality, and Large Language Models

2024-09-18
By Zhao, Ding

[PDF-215.79 KB]

English

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Details:

Creators:

Zhao, Ding
Corporate Creators:

Mobility21, Carnegie Mellon University
Corporate Contributors:

Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology

Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology Less -
Subject/TRT Terms:

[+]

Artificial Intelligence Autonomous Vehicles Connected Vehicles Forecasting Machine Learning Safety Test Procedures Virtual Reality
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

Mobility21, Carnegie Mellon University
Abstract:

The goal of this project is to study the critical scenario generation, design a testing framework, and use the generated scenarios to help the design of autonomous vehicle proving ground infrastructure aiming at the connected and autonomous vehicles evaluation. These scenarios should reflect the critical factors and risky driving conditions in the real world. Therefore, this objective encompasses the utilization of real-world datasets and the reasoning ability of large language models (LLMs).
Format:

PDF
Funding:

69A3552344811
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha-512:5ac92082974708296af16b5ce6dbc44fb4d17cc6994bbca0782c182fbc9788d3f594bb88b30012dd2d98f18db9ea26d35c1f5bf6211cd4309edab7f7ba331423
Download URL:

https://rosap.ntl.bts.gov/view/dot/78030/dot_78030_DS1.pdf
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Generating Safety-Critical Driving Scenarios for the Design of the CAV Proving-Ground – Using Domain Knowledge, Causality, and Large Language Models

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