Enhancing Autonomous Driving with Reinforcement Learning and LIDAR-Based Object Detection

Transportation could be revolutionized by autonomous driving, but sustaining reliability in dynamic, real-world conditions is still an important challenge. In order to overcome this, the research combines Reinforcement Learning (RL) with LIDAR-based object recognition, in particular using a Deep Q-Network (DQN) to improve the formulation of driving strategies. Accurate 3D environmental mapping is made possible using LIDAR, which enhances the system's ability to identify barriers and make judgments in real time. In complicated situations, this integrated method improves obstacle detection accuracy by 30% and reduces collision rates by around 20% when compared to conventional systems that rely on camera and radar technologies. The RL-LIDAR system's real-time applicability is further demonstrated by its 35% increase in decision-making efficiency and 20% reduction in processing times. These developments point to a significant increase in the dependability and security of autonomous cars in difficult situations. In order to further improve system performance, future research will concentrate on expanding this strategy to real-world applications and integrating new sensor technologies.