Time-Efficient Dynamic Urban Global Planner

This paper presents a global route planning algorithm designed as part of the navigation module embedded in an Autonomous Driving System (ADS). Unlike trajectory planners, which focus on local maneuvering and vehicle control, this algorithm determines optimal routes at a higher level, prioritizing dynamic adaptation to traffic conditions and regulatory elements. The planner aims to minimize travel time rather than merely reducing the total distance traveled, making it particularly effective in urban environments where traffic signals, vehicle interactions, and road regulations significantly impact journey duration. To achieve this, the algorithm dynamically adjusts to real-time variations in traffic flow and control measures. Additionally, it integrates risk-aware routing by imposing penalties on roads with higher pedestrian interaction, enhancing safety and increasing public acceptance of ADS technology. Designed for efficiency and scalability, the algorithm is lightweight enough to run on microcontroller-based embedded systems, ensuring feasibility for real-world deployment in constrained computing environments. The algorithm was tested using a Renault mass-production car, demonstrating its applicability in real-world driving scenarios.