Energy-Efficient Speed Planning for Autonomous Driving in Dynamic Traffic Scenarios

In the field of autonomous driving, velocity planning is of paramount importance for handling dynamic obstacle scenarios. To avoid unnecessary acceleration and deceleration, self-driving vehicles need to find an energy-optimized velocity trajectory. Moreover, in complex traffic environments, the vehicle trajectory must consider the spatio-temporal coupling problem to avoid unrealistic driving paths. To address these challenges, this paper proposes a hierarchical planner that first plans the path and then performs speed planning based on the already planned path. Specifically, we focus on the energy consumption factor and use dynamic programming for speed planning while combining safety and comfort considerations. The optimal energy-saving trajectory is obtained by combining the speed profile with the optimal path. To cope with complex scenarios on real roads, we propose an adaptive trajectory adjustment strategy based on model predictive control to track by adaptively selecting tracking modes. Finally, hardware-in-the-loop experimental validation demonstrates that our proposed method significantly reduces energy consumption compared with the traditional decoupling method while ensuring that the autonomous vehicle adapts well to complex traffic scenarios.