Infrastructure-Assisted Rule-Based Autonomous Driving Using UWB Cooperative Localization and Communication with HD Maps

Autonomous driving systems commonly rely on cameras, LiDAR, radar, and high-performance processors, which increase cost and computational complexity. This paper proposes an infrastructure-assisted lightweight autonomous driving framework that uses Ultra-Wideband (UWB) for both cooperative localization and structured traffic-state communication, together with High-Definition (HD) maps and deterministic rule-based control. Roadside anchors provide ranging measurements and reference coordinates, while intelligent traffic lights, nearby vehicles, and pedestrians transmit compact state messages through the simulated UWB channel. The ego vehicle fuses UWB ranging with an Inertial Measurement Unit and wheel odometry, matches the estimated pose to an HD map, and determines target speeds directly from communicated signal and object states. The framework was evaluated in CARLA Town10HD using 30 runs of 10 min each. Across 594 stop-required events, 527 were handled successfully, yielding an overall success rate of 88.7%. Success rates were 91.5% for stopped leading vehicles, 85.2% for red-light approaches, and 85.7% for pedestrian crossings. The results support the feasibility of low-computation, communication-assisted rule-based driving in a simulated urban environment.