An Application of Deep Learning Techniques in Trajectory Planning of Autonomous Vehicles

This research explores trajectory planning for Autonomous Vehicles, with a focus on optimizing routes for energy efficiency, safety, and regulatory compliance. Autonomous Vehicles utilize sensors, maps, and real-time traffic data to navigate dynamic and static obstacles, ensuring the safety of all road users. Collaborative planning, enabled by vehicle-to-vehicle and vehicle- to-infrastructure communication, is emerging as a crucial aspect of autonomous vehicles development, promising to enhance traffic flow and efficiency. The study examines various machine learning techniques, particularly deep learning and reinforcement learning, employed to improve the quality and safety of autonomous vehicles trajectory planning. These techniques enable autonomous vehicles to learn from human driving behaviors, adapt to complex environments, and optimize navigation strategies. Moreover, the study highlights the significant advancements and future potential of deep learning and reinforcement learning in creating more efficient, safe, and reliable autonomous driving systems.