Volume 42 Issue 4
Aug.  2024
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Article Navigation >  Journal of Transport Information and Safety  > 2024  >  42(4): 144-153
LU Chunyi, HE Shanglu, GAO Binbin, CAO Congyong, FAN Zhengwen. A Game Theory-based Lane Change Decision Model for Automated Vehicle Leaving the Freeway Dedicated Lane for Automated Driving[J]. Journal of Transport Information and Safety, 2024, 42(4): 144-153. doi: 10.3963/j.jssn.1674-4861.2024.04.016
Citation: LU Chunyi, HE Shanglu, GAO Binbin, CAO Congyong, FAN Zhengwen. A Game Theory-based Lane Change Decision Model for Automated Vehicle Leaving the Freeway Dedicated Lane for Automated Driving[J]. Journal of Transport Information and Safety, 2024, 42(4): 144-153. doi: 10.3963/j.jssn.1674-4861.2024.04.016
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A Game Theory-based Lane Change Decision Model for Automated Vehicle Leaving the Freeway Dedicated Lane for Automated Driving

doi: 10.3963/j.jssn.1674-4861.2024.04.016

  • School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China

  • Received Date: 2024-03-09
    Available Online: 2024-11-25
    Abstract
  • Under the condition where there is dedicated lane for autonomous vehicles on the freeway, autonomous vehicles need to conduct a consequence lane-changing maneuvers to leave the dedicated lane as well as the freeway mainline. These series of actions would bring the conflicts between autonomous vehicles and human-driving vehicles, which would increase the risk of collisions but decrease the traffic efficiency of diversion area. This study developed a lane-changing decision model for autonomous vehicles based on game theory and model predictive control, considering the impact of different types of lane and mixed traffic conditions. A measure, lane changing crisis, is proposed for autonomous vehicles, which is calculated based on the distance from automated vehicle to the off-ramp and the distribution of acceptable lane change gap in adjacent lanes. The driving style of human-driving vehicle is valued by the variation of acceleration. The cost functions in the model predictive control method for different types of vehicles are formulated. The optimal acceleration of autonomous vehicles at the next time interval could be predicted based on the current traffic state. The interaction between autonomous vehicle and human-driving vehicle is described by the Stackelberg game. The lane changing decision is made to maximize the benefit of autonomous vehicle when the benefit of human-driving vehicle is the largest. And the optimal acceleration of autonomous vehicle at the next time interval is also achieved. A simulation platform integrating Python and SUMO is constructed. Four experiment scenarios with different traffic densities under different lane types and mixed traffic conditions are set. And the proposed model has been also compared with the other two lane-changing models, e.g. the default model within SUMO. The results indicate that the proposed model could consistently make optimal lane-changing decision to minimize the loss of speed. And compared with the other lane-changing models, the proposed model could increase the average speed during lane changes increasing by 1.44% and 11.81%, respectively, which validate the outperformance of the proposed method.

     

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