Prior research has extensively explored Autonomous Vehicle (AV) navigation in the presence of other vehicles, however, navigation among pedestrians, who are the most vulnerable element in urban environments, has been less examined. This paper explores AV navigation in crowded, unsignalized intersections. We compare the performance of different deep reinforcement learning methods trained on our reward function and state representation. The performance of these methods and a standard rule-based approach were evaluated in two ways, first at the unsignalized intersection on which the methods were trained, and secondly at an unknown unsignalized intersection with a different topology. For both scenarios, the rule-based method achieves less than 40\% collision-free episodes, whereas our methods result in a performance of approximately 100\%. Of the three methods used, DDQN/PER outperforms the other two methods while it also shows the smallest average intersection crossing time, the greatest average speed, and the greatest distance from the closest pedestrian.

中文翻译：

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基于深度Q网络的无信号交叉口自动驾驶汽车行人防撞

先前的研究已经广泛探索了在存在其他车辆的情况下进行的自动驾驶（AV）导航，但是，在城市环境中最易受伤害的行人之间的导航受到的研究较少。本文探讨了在拥挤且无信号交叉口中的视音频导航。我们比较了在奖励功能和状态表示上训练的各种深度强化学习方法的性能。这些方法的性能和基于标准规则的方法的评估方式有两种，一种是在训练方法的无信号交叉口，另一种是在未知的无信号交叉口且拓扑不同的情况下。对于这两种情况，基于规则的方法均实现了不到40％的无碰撞事件，而我们的方法的性能约为100％。