Uncertainty-Aware Autonomous Driving System Testing with Large Language Models
Jiahui Wu · 2025
Autonomous Driving Systems (ADSs) operate in highly dynamic and uncertain environments, requiring testing methods that address both internal (e.g., algorithm randomness, sensor limitations) and external (e.g., unpredictable events, human interactions) uncertainties. However, current approaches often fall short in realistically quantifying these uncertainties, as they struggle to address the complex interplay between known and unknown factors in real-world scenarios. To address these challenges, this work explores leveraging Large Language Models (LLMs) to enhance ADS testing by incorporating human-like reasoning and domain-specific knowledge through prompt engineering, retrieval-augmented generation, and fine-tuning. By integrating LLMs with techniques like Search-Based Testing, we aim to improve testing realism, enhance efficiency, and handle uncertainties more effectively. The proposed strategies seek to develop optimized ADS testing frameworks, enabling safer and more reliable ADS deployments.