2024/12/12 by Zhaofeng Tian, Tian, Zhaofeng, Lili Xia +3
Computer Science · Engineering · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Robotic Path Planning Algorithms #Robotics (cs.RO) #Transportation and Mobility Innovations
paper · pdf · doi:10.48550/arxiv.2412.09424
openalex publication_date 2024/12/12 · openalex created_date 2024/12/14 · openalex updated_date 2026/07/28
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.