论文标题
长期决策和FRENET空间短期轨迹计划的自动驾驶框架
An Autonomous Driving Framework for Long-term Decision-making and Short-term Trajectory Planning on Frenet Space
论文作者
论文摘要
在本文中，我们提出了一个分层框架，用于在高速公路驾驶任务上进行决策和计划。我们利用智能驾驶模型（IDM和MOBIL）根据自我周围流动的交通状况产生长期决策。这些决定既可以最大程度地提高自我表现，同时尊重其他车辆的目标。短期轨迹优化是在FRENET空间上进行的，以使道路三维曲率不变。在Frenet框架上引入了一种新型的避免障碍物的方法，以实现移动障碍物。优化探索了驾驶走廊，以生成时空多项式轨迹，以安全地浏览流量并遵守BP命令。该框架还引入了启发式主管，该主管在潜在的紧急情况下确定了意外情况并重新计算每个模块。 Carla模拟中的实验表明，框架在实施与人类行为相匹配的各种驾驶方式方面的潜力和可扩展性。
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.
