Incorporating Driver Preferences Into Eco-Driving Assistance Systems Using Optimal Control

被引:17
|
作者
Fleming, James [1 ]
Yan, Xingda [2 ]
Lot, Roberto [3 ]
机构
[1] Loughborough Univ, Wolfson Sch Mech Elect & Mfg Engn, Loughborough LE11 3TT, Leics, England
[2] Univ Surrey, Dept Mech Engn Sci, Surrey GU2 7JP, England
[3] Univ Padua, Dept Ind Engn, I-35122 Padua, Italy
基金
英国工程与自然科学研究理事会;
关键词
Vehicles; Acceleration; Optimal control; Fuels; Mathematical model; Cost function; Data models; Energy efficiency; intelligent vehicles; optimal control; advanced driver assistance systems; automotive engineering; MODEL; SUPPORT; STATES;
D O I
10.1109/TITS.2020.2977882
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Recently there have been several proposals for 'eco-driving assistance systems', designed to save fuel or electrical power by encouraging behaviours such as gentle acceleration and coasting to a stop. These systems use optimal control to find driving behaviour that minimises vehicle energy losses. In this paper, we introduce a methodology to account for driver preferences on acceleration, braking, following distances and cornering speed in such eco-driving optimal control problems. This consists of an optimal control model of acceleration and braking behaviour containing several physically-meaningful parameters to describe driver preferences. If used in combination with a model of fuel or energy consumption, this can provide an adjustable trade-off between satisfying those preferences and minimising energy losses. We demonstrate that the model gives comparable performance to existing car-following and cornering models when predicting drivers' speed in these situations by comparison with real-world driving data. Finally, we present an example highway braking scenario for an electric vehicle, illustrating a trade-off between satisfying driver preferences on vehicle speed and acceleration and reducing electrical energy usage by up to 43%.
引用
收藏
页码:2913 / 2922
页数:10
相关论文
共 50 条
  • [1] Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning
    Kerbel, Lindsey
    Ayalew, Beshah
    Ivanco, Andrej
    Loiselle, Keith
    2022 AMERICAN CONTROL CONFERENCE, ACC, 2022, : 2409 - 2415
  • [2] Perceived usefulness of eco-driving assistance systems in Europe
    Trommer, S.
    Hoeltl, A.
    IET INTELLIGENT TRANSPORT SYSTEMS, 2012, 6 (02) : 145 - 152
  • [3] Driving Mode Advice for Eco-Driving Assistance System With Driver Reaction Delay Compensation
    Chen, Yutao
    Lazar, Mircea
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2022, 69 (01) : 134 - 138
  • [4] Energy-optimal control for eco-driving on curved roads
    Bentaleb, Ahmed
    El Hajjaji, Ahmed
    Rabhi, Abdelhamid
    Karama, Asma
    Benzaouia, Abdellah
    2022 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), 2022, : 1584 - 1590
  • [5] Predictive kinetic energy management for an add-on driver assistance eco-driving of heavy vehicles
    Yoon, DoHyun Daniel
    Ayalew, Beshah
    Ivanco, Andrej
    Loiselle, Keith
    IET INTELLIGENT TRANSPORT SYSTEMS, 2020, 14 (13) : 1824 - 1834
  • [6] Eco-driving command for tram-driver system
    La Delfa, Salvatore
    Enjalber, Simon
    Polet, Philippe
    Vanderhaegen, Frederic
    IFAC PAPERSONLINE, 2016, 49 (19): : 444 - 449
  • [7] Examining the effects of an eco-driving message on driver distraction
    Rouzikhah, Hossein
    King, Mark
    Rakotonirainy, Andry
    ACCIDENT ANALYSIS AND PREVENTION, 2013, 50 : 975 - 983
  • [8] The Effects of an Eco-Driving Assistance System for a City Bus on Driving Style
    Xiong, Shuo
    Xie, Hui
    Tong, Qiang
    IFAC PAPERSONLINE, 2018, 51 (31): : 331 - 336
  • [9] Robust Optimal Eco-driving Control with Uncertain Traffic Signal Timing
    Sun, Chao
    Shen, Xinwei
    Moura, Scott
    2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC), 2018, : 5548 - 5553
  • [10] Development and Field Trial of a Driver Assistance System to Encourage Eco-Driving in Light Commercial Vehicle Fleets
    Vagg, Christopher
    Brace, Chris J.
    Hari, Deepak
    Akehurst, Sam
    Poxon, John
    Ash, Lloyd
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2013, 14 (02) : 796 - 805