自动驾驶安全挑战:行为决策与运动规划  

Autonomous Driving Safety Challenge:Behavior Decision-Making and Motion Planning

作　　者：关鑫 史佳敏 陈仕韬 刘剑毅[1] 郑南宁[1] GUAN Xin;SHI Jiamin;CHEN Shitao;LIU Jianyi;ZHENG Nanning(Institute of Artificial Intelligence and Robotics,Xi′an Jiaotong University,Xi′an 710049)

机构地区：[1]西安交通大学人工智能与机器人研究所,西安710049

出　　处：《模式识别与人工智能》2023年第3期191-210,共20页Pattern Recognition and Artificial Intelligence

基　　金：科技创新2030--“新一代人工智能”重大项目(No.2021ZD0110705)资助。

摘　　要：在自动驾驶技术发展中,安全性一直作为首要因素被业界重视.行为决策与运动规划系统作为该技术的关键环节,对智慧属性具有更高要求,需要不断地随着环境变化做出当前的最优策略与行为,确保车辆行驶过程中的安全.文中分别对行为决策和运动规划系统进行深层次阐述,首先,介绍行为决策中基于规则的决策算法、基于监督学习的决策算法、基于强化学习的决策算法的算法理论及其在实车中的应用.然后,介绍运动规划中基于采样的规划算法、基于图搜索的规划算法、基于数值优化的规划算法和基于交互性的规划算法,并对算法的设计展开讨论,从安全角度分析行为决策和运动规划,对比各类方法的优缺点.最后,展望自动驾驶领域未来的安全研究方向及挑战.In the development of autonomous driving technology,safety is always regarded as a top priority.The behavior decision-making and motion planning systems,as key components of the technology,possess higher requirements for intelligence.They need continuously make optimal strategies and behaviors according to the changing environment to ensure the safety of vehicle driving.The behavior decision-making and motion planning systems are expounded.Firstly,the theory and applications of rule-based decision algorithms,supervised learning-based decision algorithms,and reinforcement learning-based decision algorithms are introduced.Then,sampling-based planning algorithms,graph search-based planning algorithms,numerical optimization-based planning algorithms and interaction-based planning algorithms in motion planning are discussed and their designs are discussed.Behavior decision-making and motion planning are analyzed from the perspective of safety,and the advantages and disadvantages of various methods are compared.Finally,future research directions and challenges for safety in the field of autonomous driving are predicted.

关 键 词：人工智能 自动驾驶 行为决策 运动规划 安全性 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]