Achieving energy-efficient trajectory planning for autonomous driving remains a challenge due to the limitations of model-agnostic approaches. This study addresses this gap by introducing an online nonlinear programming trajectory optimization framework that integrates a differentiable energy model into autonomous systems. By leveraging traffic and slope profile predictions within a safety-critical framework, the proposed method enhances fuel efficiency for both sedans and diesel trucks by 3.71\% and 7.15\%, respectively, when compared to traditional model-agnostic quadratic programming techniques. These improvements translate to a potential \$6.14 billion economic benefit for the U.S. trucking industry. This work bridges the gap between model-agnostic autonomous driving and model-aware ECO-driving, highlighting a practical pathway for integrating energy efficiency into real-time trajectory planning.

翻译：由于模型无关方法的局限性，实现自动驾驶的节能轨迹规划仍具挑战。本研究通过引入一种在线非线性规划轨迹优化框架，将可微能量模型集成到自动驾驶系统中，以弥补这一不足。通过在安全关键框架中利用交通与坡度剖面预测，所提方法相较于传统的模型无关二次规划技术，分别将轿车和柴油卡车的燃油效率提升了3.71%和7.15%。这些改进转化为美国卡车运输业潜在的61.4亿美元经济效益。本工作弥合了模型无关自动驾驶与模型感知的节能驾驶之间的差距，为将能源效率整合到实时轨迹规划中指明了一条实用路径。