Traffic Knowledge-augmented Path Planning for Autonomous Vehicles in Internet of Things

Traffic knowledge is essential for safe, efficient, and regulation-compliant autonomous driving. While existing path planning methods often encode only partial traffic rules or focus on specific scenarios, they lack adaptability to diverse and dynamic environments. The Internet of Things (IoT) enables vehicles to access rich, real-time traffic knowledge, yet most IoT-based approaches emphasize long-distance route optimization and overlook local path planning. This paper proposes a traffic knowledge-augmented path planning model (KARS) that integrates a knowledge graph representation of traffic knowledge with a reachable set-based planning framework in IoT environments. The knowledge graph systematically encodes spatial, temporal, and speed constraints, while the reachable set method enables the planner to dynamically adapt to real-time traffic knowledge. KARS is evaluated in multi-constraint simulation environments with static, semi-dynamic, and dynamic traffic knowledge settings. Compared to representative baselines, it improves driving safety by increasing traffic success rates in complex environments, raises maximum driving speed to enhance operational efficiency, and produces smoother acceleration profiles for improved ride comfort. These results validate the effectiveness of combining traffic knowledge with reachable set planning for safe, efficient, and adaptable autonomous driving in IoT-enabled urban scenarios.