A less-disturbed ecological driving strategy for connected and automated vehicles

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)

[img] Text
250848.pdf - Published Version
Available under License Creative Commons Attribution.



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
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.
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
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

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
314774Toward Energy Efficient Autonomous Vehiciles via Cloud-Aided learningDezong ZhaoEngineering and Physical Sciences Research Council (EPSRC)EP/S001956/1ENG - Aerospace Sciences