Deep Deterministic Policy Gradient Method for Autonomous Vehicle Maneuvering Through Multimodal LiDAR and RADAR Sensor Fusion

Autonomous Vehicle (AV) driving involves complex maneuvers, often constrained by poor environmental perception. While Deep Reinforcement Learning (DRL) and advanced sensor technologies like LiDAR and RADAR have improved AV performance, high dimensional sensor data poses challenges in critical tasks like lane changes and turns. To overcome these challenges, we propose a multimodal fusion of LiDAR and RADAR sensors with the Deep Deterministic Policy Gradient (DDPG) algorithm. Our approach preprocesses sensor data into low dimensional representations, enhancing the RL agent's environmental perception and decision making. This fusion, combined with Temporal Difference (TD) updates in the actor-critic network, improves maneuvering efficiency in the CARLA simulator. Results show a 100% task completion rate with adequate speed and time, achieving a 25% higher peak reward compared to state-of-the-art methods. The simulation videos for the same are available here https://www.youtube.com/playlist?list=PLnWGKVuAZgqlrdm21CEKW-S-Nr78qmkSX.