Ant colony algorithm or artificial potential field is commonly used for path planning of autonomous vehicle. However, vehicle dynamics and road adhesion coefficient are not taken into consideration. In addition, ant colony algorithm has blindness/randomness due to low pheromone concentration at initial stage of obstacle avoidance path searching progress. In this article, a new fusion algorithm combining ant colony algorithm and improved potential field is introduced making autonomous vehicle avoid obstacle and drive more safely. Controller of path planning is modeled and analyzed based on simulation of CarSim and Simulink. Simulation results show that fusion algorithm reduces blindness at initial stage of obstacle avoidance path searching progress and verifies validity and efficiency of path planning. Moreover, all parameters of vehicle are changed within a reasonable range to meet requirements of steering stability and driving safely during path planning progress.

中文翻译：

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基于新融合算法的自主车辆路径规划研究

蚁群算法或人工势场常用于自动驾驶汽车的路径规划。但是，没有考虑车辆动力学和道路附着系数。此外，蚁群算法在避障路径搜索过程的初始阶段，由于信息素浓度较低，存在盲目性/随机性。本文介绍了一种结合蚁群算法和改进势场的新融合算法，使自动驾驶汽车避开障碍物并更安全地行驶。基于CarSim和Simulink的仿真对路径规划控制器进行建模和分析。仿真结果表明，融合算法降低了避障路径搜索过程初始阶段的盲目性，验证了路径规划的有效性和效率。而且，