Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning
This addresses the challenge of safe and efficient autonomous driving in pedestrian-heavy urban settings, though it appears incremental as it builds on existing DQN methods with a multi-objective approach.
The paper tackles autonomous navigation in urban environments with pedestrians by formulating it as a multi-objective reinforcement learning problem, and the proposed method outperforms a single-objective DQN variant in all aspects in evaluations using a custom CARLA simulator environment.
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.