Abstract:

A path planning method was investigated based on the peripheral vehicle trajectory prediction with doing digital simulations to improve the driving safety and access efficiency of intelligent vehicles in dynamic driving environments. The peripheral vehicle trajectory prediction method was proposed based on the Spatio-Temporal Graph Convolutional Network (STGCN), which encoded the historical vehicle trajectories through STGCN, extracted the spatio-temporal features of traffic maps and combined with long and short-term memory networks to achieve the trajectory prediction of peripheral vehicles. On this basis, a path planning method was proposed based on an Improved Artificial Potential Field (APF), and an APF-based driving hazard evaluation module was established, which described the driving hazard by using the Frenet coordinates, and completed the path planning through the potential distribution of target obstacles and road boundaries and the gradient descent method. The results show that the proposed algorithm improves prediction accuracy by about 3% in the short-time prediction and by 1% in the long-time prediction with a path curve of the front wheel angle not exceeding 0.12 rad, and a curvature not exceeding 0.1, ensuring comfort and high efficiency during vehicle travel while effectively avoiding collisions.

Key words: intelligent vehicles, path planning, trajectory prediction, spatiotemporal graph convolutional network (STGCN), artificial potential field (APF)