LAUSR

Constrained autonomous vehicle overtaking trajectories are usually difficult to generate due to certain practical requirements and complex environmental limitations. This problem becomes more challenging when multiple contradicting objectives are required to be optimized and the on-road objects to be overtaken are irregularly placed. In this article, a novel swarm intelligence-based algorithm is proposed for producing the multiobjective optimal overtaking trajectory of autonomous ground vehicles. The proposed method solves a multiobjective optimal control model in order to optimize the maneuver time duration, the trajectory smoothness, and the vehicle visibility, while taking into account different types of mission-dependent constraints. However, one problem that could have an impact on the optimization process is the selection of algorithm control parameters. To desensitize the negative influence, a novel fuzzy adaptive strategy is proposed and embedded in the algorithm framework. This allows the optimization process to dynamically balance the local exploitation and global exploration, thereby exploring the tradeoff between objectives more effectively. The performance of using the designed fuzzy adaptive multiobjective method is analyzed and validated by executing a number of simulation studies. The results confirm the effectiveness of applying the proposed algorithm to produce multiobjective optimal overtaking trajectories for autonomous ground vehicles. Moreover, the comparison to other state-of-the-art multiobjective optimization schemes shows that the designed strategy tends to be more capable in terms of producing a set of widespread and high-quality Pareto-optimal solutions.