A Reliable Path Planning Method for Lane Change Based on Hybrid PSO-IACO Algorithm

Abstract

The real-time performance of path planning algorithms and path continuity are crucial to motion planning. Thus, B-spline-based path planners have attracted extensive interest because of control flexibility and continuous curvature. However, the B-spline-based planning requires lots of resources to solve due to the multiple nonlinear constraints. Therefore, a new hybrid algorithm is proposed, which utilizes the comple- mentary advantages of particle swarm optimization (PSO) and improved ant colony optimization (IACO), called PSO-IACO. The proposed algorithm comprises two phases. First, the PSO ensures a fast convergence to a series of feasible rough paths, which are used to initialize the pheromone allocation and the position of IACO. Then, the IACO with the advantage of positive feedback help improves the quality of the path. Moreover, the main improvement of IACO from ACO is the pheromone update strategy considering the local and global search experience, which is inspired by the idea of PSO and Max-Min ant system. Simulation demonstrates that the path quality of PSO-IACO outperforms that of PSO, IACO, Midaco, and genetic algorithm(GA). It also outperforms that of Enumeration in most scenarios. The success solution rate is improved two times as compared to Midaco for some scenarios. And the execution time is reduced to 74% in comparison with Enumeration for the large-scalescenario.