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<p>This paper presents an integrated decision-making and trajectory planning framework specifically designed for lane change maneuvers. Lane change trajectories for the host vehicle are generated using the&#xa0;Quadratic Programming by sampling lane change duration and longitudinal displacement. Subsequently, the problem is formulated as a non-cooperative Stackelberg game to model the competitive interaction between the host and obstacle vehicles. A utility function is proposed to effectively map the host vehicle’s various lane change trajectory options and the obstacle vehicle’s discrete acceleration possibilities. This facilitates the determination of optimal lane change strategies for the host and corresponding obstacle vehicle&#xa0;responses within a framework that maximizes multi-objective utility for each participant. Notably, the proposed method is designed to redirect the host vehicle to its original lane in the event of irrational behavior from the obstacle vehicle. Validation of the developed Stackelberg game against an open-source game solver confirms its reliability. Comparative experiments conducted using traffic scenarios from the HighD Dataset underscore the capacity of this integrated approach to effectively emulate human-like behaviors, thus highlighting its pragmatic utility. Lastly, the study delves into an analysis of cooperative and non-cooperative game solutions, providing valuable insights into real-world traffic dynamics.</p>

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Integrated Decision Making and Trajectory Planning of Combined Stackelberg Game and Sampling Based Method with Irrational Driver Behavior

  • 期刊论文

  • 发表日期：

    2026年6月19日

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  • Zhiqiang Zhang,
  • Balazs Kulcsar,
  • Lei Zhang,
  • Cong Wang,
  • Zhenpo Wang

摘要

This paper presents an integrated decision-making and trajectory planning framework specifically designed for lane change maneuvers. Lane change trajectories for the host vehicle are generated using the Quadratic Programming by sampling lane change duration and longitudinal displacement. Subsequently, the problem is formulated as a non-cooperative Stackelberg game to model the competitive interaction between the host and obstacle vehicles. A utility function is proposed to effectively map the host vehicle’s various lane change trajectory options and the obstacle vehicle’s discrete acceleration possibilities. This facilitates the determination of optimal lane change strategies for the host and corresponding obstacle vehicle responses within a framework that maximizes multi-objective utility for each participant. Notably, the proposed method is designed to redirect the host vehicle to its original lane in the event of irrational behavior from the obstacle vehicle. Validation of the developed Stackelberg game against an open-source game solver confirms its reliability. Comparative experiments conducted using traffic scenarios from the HighD Dataset underscore the capacity of this integrated approach to effectively emulate human-like behaviors, thus highlighting its pragmatic utility. Lastly, the study delves into an analysis of cooperative and non-cooperative game solutions, providing valuable insights into real-world traffic dynamics.