A_Less-Disturbed_Ecological_Driving_Strategy_for_Connected_and_Automated_Vehicles.pdf (1.76 MB)
A less-disturbed ecological driving strategy for connected and automated vehicles
journal contributionposted on 2023-01-24, 16:24 authored by Jinsong Yang, Dezong Zhao, Jingjing JiangJingjing Jiang, Jianglin Lan, Byron MasonByron Mason, Daxin Tian, Liang Li
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 in real driving is that the vehicle has to consider other traffic participants when tracking the reference speed. Therefore, this paper combines both longitudinal control 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 less-disturbed ecological driving 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%.
Towards Energy Efficient Autonomous Vehicles via Cloud-Aided Learning
Engineering and Physical Sciences Research CouncilFind out more...
Royal Society-Newton Advanced Fellowship under Grant NAF\R1\201213
State Key Laboratory of Automotive Safety and Energy at Tsinghua University under Project No. KF2009
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering
Published inIEEE Transactions on Intelligent Vehicles
Pages413 - 424
- VoR (Version of Record)
Rights holder© The Authors
Publisher statementThis is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/