A Survey of Motion Planning Algorithms
Yunsong Guo, Guosheng Ge, Qiming Xu, Shengguang Yang · 2023
Autonomous driving has transformed road safety and navigation, alleviating the annual toll of accidents caused by human errors. Motion planning, a critical aspect of this technology, involves calculating optimal routes, adapting to driver preferences, and addressing emergency scenarios. Cameras and radar collaborate to accurately represent the vehicle's surroundings, aiding calculations conducted by advanced onboard computers like Tesla's FSD chip. The integration of these technologies ensures efficient lane changes, overtaking maneuvers, and obstacle avoidance. Despite advancements, challenges like high-dimensional state spaces, dynamic environments, safety assurance, and complex constraints persist. Solutions to these challenges entail innovative algorithmic approaches, machine learning integration, robust sensing, and real-world testing. Implementing diverse path planning algorithms tailored to distinct road scenarios and real-time adherence to regulations and computational efficiency further enriches autonomous driving. As the field continues to evolve, the emphasis on sustainable and adaptive path planning, considering both efficiency and safety, heralds a future where autonomous driving technology flourishes within the framework of intelligent transportation systems.