Semantic Reasoning with Knowledge Graphs for Material-Aware Obstacle Handling in Autonomous Vehicles

The inability of autonomous vehicles (AVs) to infer the material properties of obstacles limits their decision-making capacity. While AVs rely on sensor systems such as cameras, LiDAR, and radar to detect obstacles, this study suggests combining sensors with a knowledge graph (KG) framework to improve AVs' comprehension of physical material qualities. Beyond sensor data, AVs can infer qualities such as malleability, density, and elasticity using a semantic KG that depicts the relationships between obstacles and their attributes. Using the CARLA autonomous driving simulator, we evaluated AV performance with and without KG integration. The findings demonstrate that the KG-based method improves obstacle management, which allows AVs to use material qualities to make better decisions about when to change lanes or apply emergency braking. For example, the KG-integrated AV changed lanes for hard impediments like traffic cones and successfully avoided collisions with flexible items such as plastic bags by passing over them. Compared to the control system, the KG framework demonstrated improved responsiveness to obstacles by resolving conflicting sensor data, causing emergency stops for 13.3% more cases. In addition, our method exhibits a 6.6% higher success rate in lane-changing maneuvers in experimental scenarios, particularly for larger, high-impact obstacles. Ultimately, our findings point to the potential of KGs and their future integration with language models (LLMs) to enhance decision-making and safety in complex driving situations, particularly for embodied agents operating in open urban environments.