Risk-Aware Driving Scenario Analysis with Large Language Models

• URL: http://arxiv.org/abs/2502.02145v1
• Date: Tue, 04 Feb 2025 09:19:13 GMT
• Title: Risk-Aware Driving Scenario Analysis with Large Language Models
• Authors: Yuan Gao, Mattia Piccinini, Johannes Betz,
• Abstract summary: Large Language Models (LLMs) can capture nuanced contextual relationships, reasoning, and complex problem-solving. This paper proposes a novel framework that leverages LLMs for risk-aware analysis of generated driving scenarios.
• Score: 7.093690352605479
• License:
• Abstract: Large Language Models (LLMs) can capture nuanced contextual relationships, reasoning, and complex problem-solving. By leveraging their ability to process and interpret large-scale information, LLMs have shown potential to address domain-specific challenges, including those in autonomous driving systems. This paper proposes a novel framework that leverages LLMs for risk-aware analysis of generated driving scenarios. We hypothesize that LLMs can effectively evaluate whether driving scenarios generated by autonomous driving testing simulators are safety-critical. To validate this hypothesis, we conducted an empirical evaluation to assess the effectiveness of LLMs in performing this task. This framework will also provide feedback to generate the new safety-critical scenario by using adversarial method to modify existing non-critical scenarios and test their effectiveness in validating motion planning algorithms. Code and scenarios are available at: https://github.com/yuangao-tum/Riskaware-Scenario-analyse

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