SVBRD-LLM: Self-Verifying Behavioral Rule Discovery for Autonomous Vehicle Identification
This work addresses the need for analyzing traffic safety and policies for autonomous vehicles, though it is incremental as it builds on existing tracking and LLM methods.
The paper tackled the problem of understanding autonomous vehicle behavior from real traffic videos by proposing SVBRD-LLM, a framework that automatically discovers and verifies interpretable behavioral rules, achieving 90.0% accuracy and 93.3% F1-score in autonomous vehicle identification.
As more autonomous vehicles operate on public roads, understanding real-world behavior of autonomous vehicles is critical to analyzing traffic safety, making policies, and public acceptance. This paper proposes SVBRD-LLM, a framework that automatically discovers, verifies, and applies interpretable behavioral rules from real traffic videos through zero-shot prompt engineering. The framework extracts vehicle trajectories using YOLOv8 and ByteTrack, computes kinematic features, and employs GPT-5 zero-shot prompting to compare autonomous and human-driven vehicles, generating 35 structured behavioral rule hypotheses. These rules are tested on a validation set, iteratively refined based on failure cases to filter spurious correlations, and compiled into a high-confidence rule library. The framework is evaluated on an independent test set for speed change prediction, lane change prediction, and autonomous vehicle identification tasks. Experiments on over 1500 hours of real traffic videos show that the framework achieves 90.0% accuracy and 93.3% F1-score in autonomous vehicle identification. The discovered rules clearly reveal distinctive characteristics of autonomous vehicles in speed control smoothness, lane change conservativeness, and acceleration stability, with each rule accompanied by semantic description, applicable context, and validation confidence.