We propose a safe DRL approach for autonomous vehicle (AV) navigation through crowds of pedestrians while making a left turn at an unsignalized intersection. Our method uses two long-short term memory (LSTM) models that are trained to generate the perceived state of the environment and the future trajectories of pedestrians given noisy observations of their movement. A future collision prediction algorithm based on the future trajectories of the ego vehicle and pedestrians is used to mask unsafe actions if the system predicts a collision. The performance of our approach is evaluated in two experiments using the high-fidelity CARLA simulation environment. The first experiment tests the performance of our method at intersections that are similar to the training intersection and the second experiment tests our method at intersections with a different topology. For both experiments, our methods do not result in a collision with a pedestrian while still navigating the intersection at a reasonable speed.

翻译：我们建议了一种安全DRL方法,用于通过行人群通过行人群进行自动车辆导航,同时在未发信号的十字路口左转。我们的方法使用两种长短的内存模型,这些模型经过训练,能够产生对环境和行人未来轨迹的认知状态和行人对行车轨迹的观察,因为对行人行车的行车方式进行吵闹的观察。如果系统预测发生碰撞,则使用基于自我车辆和行人未来轨迹的未来碰撞预测算法来掩盖不安全的行动。我们的方法的性能通过两个实验来评估,两个实验中使用高不洁的CARLA模拟环境。第一次实验测试了我们方法在与培训十字路口相似的交叉点的性能,第二次实验测试了我们与不同地形交叉点的行人方法。对于这两种实验,我们的方法在仍然以合理速度在十字路口航行时不会导致与行人碰撞。