EMATO: Energy-Model-Aware Trajectory Optimization for Autonomous Driving

Autonomous driving currently lacks robust evidence of energy efficiency when using energy-model-agnostic trajectory planning. To address this, we explore how differential energy models can be effectively utilized under varying driving conditions to enhance energy efficiency. Furthermore, we propose an online nonlinear programming approach that optimizes polynomial trajectories generated by the Frenet polynomial method while incorporating traffic trajectory data and road slope predictions. Through case studies, quantitative analyses, and ablation studies conducted on both sedan and truck models, we demonstrate the effectiveness of the proposed method. [Video]