通过可微分能效模型，实现自动驾驶实时节能路径规划。
Slope Considered Online Nonlinear Trajectory Planning with Differential Energy Model for Autonomous Driving
- 引入可微分能效模型，结合坡度预测进行在线非线性轨迹优化。
- 相比传统方法，轿车和柴油卡车油耗分别降低3.71%和7.15%。
- 适合关注自动驾驶节能与实时路径规划的工程师与研究者。
由于模型无关方法的局限性，实现自动驾驶的节能路径规划仍具挑战。本文提出一种在线非线性规划轨迹优化框架，将可微分能效模型集成至自动驾驶系统中。通过在安全关键框架内融合交通与坡度预测，该方法相较传统模型无关二次规划技术，使轿车和柴油卡车的燃油效率分别提升3.71%和7.15%。这一改进可为美国卡车行业带来约61.4亿美元的潜在经济收益。该工作弥合了模型无关自动驾驶与模型感知节能驾驶之间的差距，为实时轨迹规划中集成能效提供了可行路径。
原文摘要 · Abstract (English)
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.
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