In this paper, we present a hierarchical framework for decision-making and planning on highway driving tasks. We utilized intelligent driving models (IDM and MOBIL) to generate long-term decisions based on the traffic situation flowing around the ego. The decisions both maximize ego performance while respecting other vehicles' objectives. Short-term trajectory optimization is performed on the Frenet space to make the calculations invariant to the road's three-dimensional curvatures. A novel obstacle avoidance approach is introduced on the Frenet frame for the moving obstacles. The optimization explores the driving corridors to generate spatiotemporal polynomial trajectories to navigate through the traffic safely and obey the BP commands. The framework also introduces a heuristic supervisor that identifies unexpected situations and recalculates each module in case of a potential emergency. Experiments in CARLA simulation have shown the potential and the scalability of the framework in implementing various driving styles that match human behavior.
翻译:在本文中,我们为高速公路驾驶任务的决策和规划提供了一个等级框架。我们利用智能驾驶模型(IDM和MOBIL)来产生基于自身交通状况的长期决定;决定在尊重其他车辆目标的同时,最大限度地发挥自我性能;在Frenet空间进行短期轨迹优化,对道路三维曲线进行变量计算;在Frenet框架中为移动障碍引入了新的障碍避免办法。优化探索了驾驶走廊,以生成安全通过交通和服从BP指令的波段多轨道。框架还引入了一位超常督导员,该督导员将查明意外情况,并在出现潜在紧急情况时对每个模块进行重新计算。CARLA模拟实验显示,在采用符合人类行为的各种驾驶风格方面,框架具有潜力和可扩展性。