Urban autonomous driving in the presence of pedestrians as vulnerable road users is still a challenging and less examined research problem. This work formulates navigation in urban environments as a multi objective reinforcement learning problem. A deep learning variant of thresholded lexicographic Q-learning is presented for autonomous navigation amongst pedestrians. The multi objective DQN agent is trained on a custom urban environment developed in CARLA simulator. The proposed method is evaluated by comparing it with a single objective DQN variant on known and unknown environments. Evaluation results show that the proposed method outperforms the single objective DQN variant with respect to all aspects.

翻译：在行人作为脆弱的道路使用者的情况下,城市自主驾驶仍然是一个挑战性的问题,研究较少。这项工作将城市环境中的导航视为一个多目标强化学习问题。为行人之间的自主导航,介绍了一个入门字典学的深层次学习变体。多目标DQN代理器接受了在CARLA模拟器中开发的定制城市环境的培训。对拟议方法的评价是将其与关于已知和未知环境的单一目标DQN变体进行比较。评价结果显示,拟议方法在所有方面都优于单一目标DQN变体。