Safe and Energy-Efficient Jerk-Controlled Speed Profiling for On-Road Autonomous Vehicles

Efficient speed planning is crucial for the safe and comfortable navigation of autonomous vehicles in dynamic environments. This paper introduces a novel energy-efficient, jerk-controlled speed planning approach based on quintic polynomial generation. We present a systematic methodology to determine the dynamic speed of autonomous vehicles by integrating several factors, including the relative velocity with dynamic obstacles, the curvature of the base frame and optimal selected path, road adherence, and road gradient. The direct integration of road adherence and gradient into the speed profiling approach contributes to improving vehicle safety. Comparative analysis with literature methods demonstrates the significant impact of jerk smoothness on energy efficiency. Simulations are conducted in a joint simulation between Simulink/Matlab and SCANeR Studio vehicle dynamics simulator, followed by validation on a real-world dataset. Our findings elucidate the significance of the proposed planning method in enhancing safety, energy economy, driving comfort, and computational efficiency, while effectively addressing a wide range of critical situations.