An application of artificial intelligence in trajectory planning

Abstract

Autonomous vehicle Development has accelerated recently, yet trajectory planning remains one of its most technically demanding components. A proper trajectory planning algorithm is essential for achieving safety, comfort, and immediate response capabilities in autonomous transportation systems operating in complex or changing environmental situations. The Research evaluates three typical trajectory planning methods: sampling-based, search-based algorithms, and optimization models against AI-driven planning systems. Traditional methods encounter three main drawbacks: insufficient mesh density control, the inability to efficiently adapt, and the failure to process real-time operations. By contrast, AI models—particularly those employing deep reinforcement learning—demonstrate significant robustness, generalizability, and dynamic adaptability advantages. System training through rewards allows artificial intelligence systems to produce navigational movements that respond well to multiple system elements, including dynamic obstacles, meteorological conditions, and multi-dimensional spatial restrictions. These Research findings demonstrate that AI trajectory planning methods bring revolutionary changes to autonomous driving improvement. Traditional methods have significant shortcomings, but the new approach solves these problems and provides vehicles with better efficiency and safety in unknown environments. Research into AI algorithm refinement should focus on enhancing transparency features, making systems more efficient and suitable for real-world applications.