Slope Considered Online Nonlinear Trajectory Planning with Differential Energy Model for Autonomous Driving
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913609650536448 |
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| author | Tian, Zhaofeng Xia, Lichen Shi, Weisong |
| author_facet | Tian, Zhaofeng Xia, Lichen Shi, Weisong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09424 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Slope Considered Online Nonlinear Trajectory Planning with Differential Energy Model for Autonomous Driving Tian, Zhaofeng Xia, Lichen Shi, Weisong Robotics 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. |
| title | Slope Considered Online Nonlinear Trajectory Planning with Differential Energy Model for Autonomous Driving |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.09424 |