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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2020  >  20(5): 58-72
YANG Lan, ZHAO Xiang-mo, WU Guo-yuan, XU Zhi-gang, MATTHEW Barth, HUI Fei, HAO Peng, HAN Meng-jie, ZHAO Zhou-qiao, FANG Shan, JING Shou-cai. Review on connected and automated vehicles based cooperative eco-driving strategies[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 58-72. doi: 10.19818/j.cnki.1671-1637.2020.05.004
Citation: YANG Lan, ZHAO Xiang-mo, WU Guo-yuan, XU Zhi-gang, MATTHEW Barth, HUI Fei, HAO Peng, HAN Meng-jie, ZHAO Zhou-qiao, FANG Shan, JING Shou-cai. Review on connected and automated vehicles based cooperative eco-driving strategies[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 58-72. doi: 10.19818/j.cnki.1671-1637.2020.05.004
shu

Review on connected and automated vehicles based cooperative eco-driving strategies

doi: 10.19818/j.cnki.1671-1637.2020.05.004
  • 1.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    Center for Environmental Research and Technology, University of California at Riverside, Riverside CA 92521, California, USA

Funds:

National Key Research and Development Program of China 2018YFB1600604

National Natural Science Foundation of China 61703053

National Natural Science Foundation of China 61973045

China Postdoctoral Science Foundation 2017M623091

Shaanxi Province Key Research and Development Program 2018ZDCXLGY-05-01

Shaanxi Province Postdoctoral Science Foundation 2018BSHYDZZ64

Natural Science Foundation of Shaanxi Province 2018JQ6035

Fundamental Research Funds for the Central Universities 300102240203

More Information
  • Author Bio:

    YANG Lan(1985-), female, seniorengineer, PhD, lanyang@chd.edu.cn

  • Corresponding author: WU Guo-yuan(1979-), male, associate professor, PhD, gywu@cert.ucr.edu
  • Received Date: 2020-04-23
  • Publish Date: 2020-10-25
    Abstract
  • To track the research progress of connected and automated vehicles(CAV) based cooperative eco-driving strategies in recent years, the influences of four factors, including the vehicle, driver behavior, traffic network and social factor on the energy consumption of CAV were analyzed. The current ecological studies on CAV were classified with vehicle, infrastructure and traveler as objects. The status-quo of 5 representative types of cooperative eco-driving scenarios were emphatically analyzed, including the eco-approach and departure at the signalized intersection, eco-cooperative adaptive cruise control, eco-cooperative driving in the ramp merging area, eco-cooperative lane changing trajectory planning and eco-routing. Analysis result shows that compared with the human driving mode, CAVs can save up to 63% fuel consumption at any traffic flow with 100% penetration rate of CAV as well as in the light traffic condition with partial penetration rate of CAV. CAVs with partial automated and connected levels can save at least 7% fuel consumption. Few existing studies consider the trajectory tracking deviation caused by the driver's response delay and automatic controller transmission delay in the case of human-machine co-driving. The existing researches assume the vehicle-to-vehicle communication(V2V) and vehicle-to-infrastructure communication(V2I) as the ideal data interaction processes. The impacts of factors such as the communication topology, transmission delay, communication failure and packet loss on the CAV based cooperative eco-driving strategies are ignored. Few existing studies discuss the eco-driving strategies in these traffic scenarios, such as the multi-lanes, shared lanes for turning and through at intersection, U-turn, as well as the mixed traffic conditions of different automated and connected level CAVs coexisting with human-driven vehicles, pedestrians and bicycles. Limited by the immaturity and imperfection of automatic driving technology and infrastructure, the test and verification work in real traffic scenarios is not carried out. The vehicle control, V2V communication, multi-vehicles collaboration, mixed traffic flow scenario, hardware-in-the-loop simulation test and real traffic scenario test will be the further development direction of CAV based cooperative eco-driving strategies.

     

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