基于能量模型优化自动驾驶轨迹，提升能效。
EMATO: Energy-Model-Aware Trajectory Optimization for Autonomous Driving
- 融合道路坡度与交通轨迹预测，优化多项式路径。
- 实测轿车与卡车均显著降低能耗，能效提升明显。
- 适合关注自动驾驶能效优化的研究者与工程师。
自动驾驶缺乏对能源效率的强证明，因现有轨迹规划未考虑能量模型。本研究提出一种在线非线性规划方法，基于Frenet多项式生成的轨迹，结合交通轨迹与道路坡度预测，实现能量模型感知的轨迹优化。进一步探究了不同驾驶条件下能量模型的利用方式，以提升能效。通过轿车与卡车的案例研究、定量分析及消融实验，验证了该方法的有效性。
原文摘要 · Abstract (English)
Autonomous driving lacks strong proof of energy efficiency with the energy-model-agnostic trajectory planning. To achieve an energy consumption model-aware trajectory planning for autonomous driving, this study proposes an online nonlinear programming method that optimizes the polynomial trajectories generated by the Frenet polynomial method while considering both traffic trajectories and road slope prediction. This study further investigates how the energy model can be leveraged in different driving conditions to achieve higher energy efficiency. Case studies, quantitative studies, and ablation studies are conducted in a sedan and truck model to prove the effectiveness of the method.
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