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.
@article{arxiv.2412.09424,
title = {Slope Considered Online Nonlinear Trajectory Planning with Differential Energy Model for Autonomous Driving},
author = {Zhaofeng Tian and Lichen Xia and Weisong Shi},
journal= {arXiv preprint arXiv:2412.09424},
year = {2024}
}