[2511.14977] SVBRD-LLM: Self-Verifying Behavioral Rule Discovery for Autonomous Vehicle Identification

Computer Science > Robotics arXiv:2511.14977 (cs) [Submitted on 18 Nov 2025 (v1), last revised 23 Mar 2026 (this version, v3)] Title:SVBRD-LLM: Self-Verifying Behavioral Rule Discovery for Autonomous Vehicle Identification Authors:Xiangyu Li, Tianyi Wang, Junfeng Jiao, Christian Claudel, Zhaomiao Guo View a PDF of the paper titled SVBRD-LLM: Self-Verifying Behavioral Rule Discovery for Autonomous Vehicle Identification, by Xiangyu Li and 4 other authors View PDF HTML (experimental) Abstract:As autonomous vehicles (AVs) are increasingly deployed on public roads, understanding their real-world behaviors is critical for traffic safety analysis and regulatory oversight. However, many data-driven methods lack interpretability and cannot provide verifiable explanations of AV behavior in mixed traffic. This paper proposes SVBRD-LLM, a self-verifying behavioral rule discovery framework that automatically extracts interpretable behavioral rules from real-world traffic videos through zero-shot large language model (LLM) reasoning. The framework first derives vehicle trajectories using YOLOv26-based detection and ByteTrack-based tracking, then computes kinematic features and contextual information. It then employs GPT-5 zero-shot prompting to perform comparative behavioral analysis between AVs and human-driven vehicles (HDVs) across lane-changing and normal driving behaviors, generating 26 structured rule hypotheses that comprises both numerical thresholds and statistical behavioral ...