Yang, J., Zhao, D. , Jiang, J., Lan, J. , Mason, B., Tian, D. and Li, L. (2023) A less-disturbed ecological driving strategy for connected and automated vehicles. IEEE Transactions on Intelligent Vehicles, 8(1), pp. 413-424. (doi: 10.1109/TIV.2021.3112499)
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Abstract
This paper proposes a less-disturbed ecological driving strategy for connected and automated vehicles (CAVs). The proposed strategy integrates the offline planning and the online tracking. In offline planning, an energy efficient reference speed is created based on traffic information (such as the average traffic speed) and characteristics of the vehicle (such as the engine efficiency map) via dynamic programming. The consideration of average traffic speed in speed planning avoids selfish optimisations. In online tracking, model predictive control is employed to update the vehicle speed in real-time to track the reference speed. A key challenge in applying ecological driving strategies is that the vehicle has to consider other traffic participants when tracking the reference speed. Therefore, this paper combines both longitudinal and lateral control to achieve better speed tracking by overtaking the preceding vehicle when necessary. The proposed less-disturbed ecological driving strategy has been evaluated in simulations in both single road segment scenario and real traffic environment. Comparisons of the proposed method with benchmark strategies and human drivers are made. The results demonstrate that the proposed strategy is more effective in energy saving. Compared to human drivers, the less-disturbed eco-driving strategy improves the fuel efficiency of CAVs by 4.53%.
Item Type: | Articles |
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Additional Information: | This work was supported in part by the EPSRC Innovation Fellowship of the Engineering and Physical Sciences Research Council of U.K. under Grant EP/S001956/1, in part by the Royal Society-Newton Advanced Fellowship under Grant NAF\R1\201213 and in part by the State Key Laboratory of Automotive Safety and Energy at Tsinghua University under Project No. KF2009. |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Zhao, Dr Dezong and Li, Dr Liang and Lan, Dr Jianglin and Yang, Jinsong |
Authors: | Yang, J., Zhao, D., Jiang, J., Lan, J., Mason, B., Tian, D., and Li, L. |
College/School: | College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity |
Journal Name: | IEEE Transactions on Intelligent Vehicles |
Publisher: | IEEE |
ISSN: | 2379-8858 |
ISSN (Online): | 2379-8904 |
Published Online: | 14 September 2021 |
Copyright Holders: | Copyright © 2021 The Authors |
First Published: | First published in IEEE Transactions on Intelligent Vehicles 8(1): 413-424 |
Publisher Policy: | Reproduced under a Creative Commons License |
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