A primary task in autonomous driving is to design a control algorithm that presents an effective and yet human-compatible behavior. To this aim, present paper considers the problem of autonomous overtaking under both state and environment constraints with dynamic surrounding vehicles. The solution is formulated based on quadratic programming optimization and is solved in receding horizon fashion. The proposed method evaluates the traffic condition online and executes a safe trajectory of an overtaking maneuver with on-coming traffic. To better incorporate traffic participants’ behaviors, a dynamic predictive model-based reachability analysis of the surrounding vehicles is utilized in the design. Reachable sets aim to ensure the drivability of the planned motions, as well as producing drivable collision-free trajectories. To this end, forward reachable sets are employed by predicting traffic vehicles’ future actual and worst-case behaviors in the design, in which the autonomous vehicle determines its trajectory accordingly. A simulation scenario is tested via MATLAB/Simulink for autonomous overtaking and the effectiveness of the proposed method is shown, demonstrating the potential utility of the present approach for implementation as an advanced driver assistance system (ADAS) in next-generation vehicles.

自动驾驶的一项主要任务是设计一种控制算法，以呈现有效且与人类兼容的行为。为此，本文考虑了动态周围车辆在状态和环境约束下的自主超车问题。该解决方案是基于二次规划优化制定的，并以后退水平方式求解。所提出的方法在线评估交通状况，并在迎面而来的交通中执行超车机动的安全轨迹。为了更好地结合交通参与者的行为，设计中使用了基于动态预测模型的周围车辆可达性分析。可达集旨在确保计划运动的可驾驶性，以及产生可驾驶的无碰撞轨迹。为此，通过在设计中预测交通车辆未来的实际和最坏情况行为，采用前向可达集，其中自动驾驶车辆相应地确定其轨迹。通过 MATLAB/Simulink 对自动超车的仿真场景进行了测试，并显示了所提出方法的有效性，证明了本方法在下一代车辆中作为高级驾驶员辅助系统 (ADAS) 实施的潜在效用。