OPTIMIZING AUTONOMOUS VEHICLE PATH PLANNING USING REINFORCEMENT LEARNING AND DYNAMIC MAPPING

Path planning is essential for autonomous driving, enabling secure and effective navigation in intricate and dynamic settings. This research examines the combination of Reinforcement Learning (RL) with dynamic mapping to enhance route planning in autonomous vehicles (AVs). RL enables AVs to ascertain ideal routes by persistently adjusting to evolving situations via trial and error, improving real-time decision-making skills. Dynamic mapping offers real-time updates on road conditions, traffic, and impediments, allowing AVs to modify their routes depending on the latest information. Integrating RL with dynamic mapping improves the vehicle's capacity to react to unforeseen conditions, such as traffic congestion or abrupt barriers, facilitating smoother and more effective navigation. This research examines the principal advantages of this integrated technique, including enhanced flexibility, augmented safety, and superior route optimization. It also tackles implementation issues and prospective developments in AV route planning using these technologies.