论文标题
B-GAP：自动驾驶的行为丰富的模拟和导航
B-GAP: Behavior-Rich Simulation and Navigation for Autonomous Driving
论文作者
论文摘要
我们在密集的模拟交通环境中解决了由具有不同驾驶员行为的公路代理所填充的密集模拟的交通环境中的问题。由于代理商的异质行为引起的行为不可预测性，因此在这种环境中的导航是具有挑战性的。我们提出了一种新的仿真技术，该技术包括通过与不同水平的侵略性相对应的富含行为的轨迹来丰富现有的流量模拟器。我们在驾驶员行为建模算法的帮助下生成这些轨迹。然后，我们使用富集的模拟器来训练由一组高级车辆控制命令组成的深入加固学习（DRL）策略，并在测试时间使用此策略来执行密集的流量进行本地导航。我们的政策隐含地对交通代理和计算自我车辆的安全轨迹的相互作用进行了模型，这是对诸如超越，超速，编织和突然的车道变化之类的积极驾驶演习的核能。我们增强的行为丰富的模拟器可用于生成由与各种驱动器行为和流量密度相对应的轨迹组成的数据集，并且我们的基于行为的导航方案可以与最先进的导航算法结合使用。
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.
