避障
障碍物
避碰
运动规划
路径(计算)
领域(数学)
势场
计算机科学
智能交通系统
马尔可夫决策过程
碰撞
模拟
马尔可夫过程
实时计算
工程类
运输工程
移动机器人
人工智能
计算机安全
机器人
计算机网络
数学
地质学
统计
法学
纯数学
政治学
地球物理学
作者
Jiufei Luo,Sijun Li,Haiqing Li,Fuhao Xia
标识
DOI:10.1177/09544062221085886
摘要
Autonomous obstacle avoidance and decision-making algorithms for intelligent connected vehicles in a complicated transportation environment are important studies on intelligent driving. However, it is difficult to adapt to a more complicated traffic environment based on safety distance and conventional potential field. Therefore, in this paper, a driving risk field model based on field theory is proposed involving transportation factors and vehicle conditions. A hidden Markov model was used to evaluate and determine the motion state of surrounding vehicles. A safe, feasible, and smooth collision-free path was planned by calculating the magnitude of the potential field forces on the longitudinal and lateral sides of the obstacle vehicles. The results showed that the method can effectively select a suitable path for obstacle avoidance in complex road conditions while satisfying safety and traffic laws.
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