Cooperative Time and Energy-Optimal Lane Change Maneuvers for Connected Automated Vehicles

被引:17
作者
Chen, Rui [1 ,2 ]
Cassandras, Christos G. [1 ,2 ]
Tahmasbi-Sarvestani, Amin [3 ,4 ]
Saigusa, Shigenobu [3 ]
Mahjoub, Hossein Nourkhiz [3 ]
Al-Nadawi, Yasir Khudhair [3 ]
机构
[1] Boston Univ, Div Syst Engn, Boston, MA 02446 USA
[2] Boston Univ, Ctr Informat & Syst Engn, Boston, MA 02446 USA
[3] Honda Res & Dev Amer Inc, Ann Arbor, MI 48103 USA
[4] Optimus Ride Inc, Boston, MA 02210 USA
关键词
Autonomous vehicles; intelligent vehicles; cooperative systems; optimal control; ROADS;
D O I
10.1109/TITS.2020.3036420
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
We derive optimal control policies for a Connected Automated Vehicle (CAV) cooperating with neighboring CAVs in order to implement a lane change maneuver consisting of a longitudinal phase where the CAV properly positions itself relative to the cooperating neighbors and a lateral phase where it safely changes lanes. For the first phase, we optimize the maneuver time subject to safety constraints and subsequently minimize the associated surrogate energy consumption of all cooperating vehicles in this maneuver. For the second phase, we jointly optimize time and energy approximation and provide three different solution methods including a real-time approach based on Control Barrier Functions (CBFs). We prove structural properties of the optimal policies which simplify the solution derivations and, in the case of the longitudinal maneuver, lead to analytical optimal control expressions. The solutions, when they exist, are guaranteed to satisfy safety constraints for all vehicles involved in the maneuver. Simulation results where the controllers are implemented show their effectiveness in terms of significant performance improvements compared to maneuvers performed by human-driven vehicles.
引用
收藏
页码:3445 / 3460
页数:16
相关论文
共 33 条
  • [1] Ames AD, 2012, IEEE DECIS CONTR P, P6837, DOI 10.1109/CDC.2012.6426229
  • [2] [Anonymous], 2018, APPL OPTIMAL CONTROL
  • [3] Road safety knowledge and policy: A historical institutional analysis of the Netherlands
    Bax, Charlotte
    Leroy, Pieter
    Hagenzieker, Marjan P.
    [J]. TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2014, 25 : 127 - 136
  • [4] Caraffi C, 2012, IEEE INT C INTELL TR, P975, DOI 10.1109/ITSC.2012.6338748
  • [5] Chen R, 2019, IEEE DECIS CONTR P, P2220, DOI 10.1109/CDC40024.2019.9029749
  • [6] A multiagent approach to autonomous intersection management
    Dresner, Kurt
    Stone, Peter
    [J]. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2008, 31 : 591 - 656
  • [7] Adaptive Quasi-Dynamic Traffic Light Control
    Fleck, Julia L.
    Cassandras, Christos G.
    Geng, Yanfeng
    [J]. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2016, 24 (03) : 830 - 842
  • [8] Path Planning and Cooperative Control for Automated Vehicle Platoon Using Hybrid Automata
    Huang, Zichao
    Chu, Duanfeng
    Wu, Chaozhong
    He, Yi
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 20 (03) : 959 - 974
  • [9] Model Predictive Control of Vehicles on Urban Roads for Improved Fuel Economy
    Kamal, Md Abdus Samad
    Mukai, Masakazu
    Murata, Junichi
    Kawabe, Taketoshi
    [J]. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2013, 21 (03) : 831 - 841
  • [10] Katriniok A, 2013, 2013 EUROPEAN CONTROL CONFERENCE (ECC), P974