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. (2021) A less-disturbed ecological driving strategy for connected and automated vehicles. IEEE Transactions on Intelligent Vehicles, (doi: 10.1109/TIV.2021.3112499) (Early Online Publication)

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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
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:Early Online Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Zhao, Dr Dezong and Yang, Jinsong and Li, Dr Liang and Lan, Dr Jianglin
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 2021
Publisher Policy:Reproduced under a Creative Commons License

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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