We address the problem of ego-vehicle navigation in dense simulated traffic environments populated by road agents with varying driver behaviors. Navigation in such environments is challenging due to unpredictability in agents' actions caused by their heterogeneous behaviors. We present a new simulation technique consisting of enriching existing traffic simulators with behavior-rich trajectories corresponding to varying levels of aggressiveness. We generate these trajectories with the help of a driver behavior modeling algorithm. We then use the enriched simulator to train a deep reinforcement learning (DRL) policy that consists of a set of high-level vehicle control commands and use this policy at test time to perform local navigation in dense traffic. Our policy implicitly models the interactions between traffic agents and computes safe trajectories for the ego-vehicle accounting for aggressive driver maneuvers such as overtaking, over-speeding, weaving, and sudden lane changes. Our enhanced behavior-rich simulator can be used for generating datasets that consist of trajectories corresponding to diverse driver behaviors and traffic densities, and our behavior-based navigation scheme can be combined with state-of-the-art navigation algorithms.
翻译:我们提出一种新的模拟技术,通过不同程度的攻击性轨迹来丰富现有的交通模拟器。我们在驱动行为模型算法的帮助下生成这些轨迹。然后,我们使用浓缩模拟器来培训由一组高水平车辆控制指令组成的深度强化学习(DRL)政策,并在测试时使用这一政策进行密集交通的当地导航。我们的政策隐含地模拟了交通代理器和计算自驾驶器安全轨迹的相互作用,以进行超载、超速、编织和突如其来的航道变化等具有攻击性的驾驶器动作。我们增强的行为丰富模拟器可用于生成由不同驾驶器行为和交通密度对应的轨迹组成的数据集,而我们基于行为的导航计划可以与州级导航算法相结合。