Chai Runqi, Tsourdos Antonios, Savvaris Al, Chai Senchun, Xia Yuanqing, Philip Chen C L
IEEE Trans Cybern. 2021 Aug;51(8):4035-4049. doi: 10.1109/TCYB.2020.2973748. Epub 2021 Aug 4.
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.
由于某些实际要求和复杂的环境限制，受限自动驾驶车辆的超车轨迹通常难以生成。当需要优化多个相互矛盾的目标且要超车的道路物体放置不规则时，这个问题变得更具挑战性。在本文中，提出了一种基于新型群体智能的算法，用于生成自主地面车辆的多目标最优超车轨迹。所提出的方法求解一个多目标最优控制模型，以优化操纵持续时间、轨迹平滑度和车辆可见性，同时考虑不同类型的任务相关约束。然而，一个可能影响优化过程的问题是算法控制参数的选择。为了降低负面影响，提出了一种新型模糊自适应策略并将其嵌入算法框架中。这使得优化过程能够动态平衡局部利用和全局探索，从而更有效地探索目标之间的权衡。通过进行大量的仿真研究，分析并验证了使用所设计的模糊自适应多目标方法的性能。结果证实了应用所提出的算法为自主地面车辆生成多目标最优超车轨迹的有效性。此外，与其他最新的多目标优化方案的比较表明，所设计的策略在生成一组广泛且高质量的帕累托最优解方面往往更具能力。
