Multi-Rules Reachability Analysis for Road Agents Using Graph-Based Maps and Real-Time Kinematics

Automated vehicles perform well in simple environments with clear rules, but urban traffic presents significant challenges due to the unpredictable behavior of road users, sometimes beyond traffic rules. Achieving full autonomy in such settings requires a systematic approach to modeling the possible actions of all agents. This paper presents a multi-rules reachability analysis framework that integrates graph-based maps with real-time perception data to dynamically characterize the surrounding space. By leveraging the semantic richness and modularity of Lanelet2 maps, our method provides a structured representation that enhances situational awareness. This allows for the extraction of navigation-relevant information, with the goal of supporting safer and more efficient decision-making in complex urban environments.