Testing Autonomous Driving Systems with Irregular Junctions Extracted from OpenStreetMap
Tiantian Sun, Changwen Li, Rongjie Yan, Yan Cai · 2025
Testing autonomous driving systems (ADS) presents significant challenges in trajectory planning, route control, and collision avoidance, particularly at complex junctions. Among these, irregular junctions are especially valuable for exposing ADS weaknesses—yet they remain difficult to generate systematically. Since manually configured irregular junctions may not accurately reflect real-world conditions, a more practical approach is to identify existing irregular junctions. This paper presents a method for extracting irregular junctions from global OpenStreetMap (OSM) data and generating safety-critical scenarios based on them. By analyzing junction topologies and quantifying them with a difficulty metric, our approach uncovers challenging scenarios that effectively expose ADS defects. Experimental evaluations with various autopilots show that the identified junctions and generated scenarios trigger unique ADS defects more effectively than simulator-provided ones.