结合状态机和动态目标路径的无人驾驶决策仿真  

Decision making simulation of autonomous driving combined with state machine and dynamic target path

作　　者：范鑫淼 何武 张梓培[1,2] Fan Xinmiao;He Wu;Zhang Zipei(College of Movie and Media,Sichuan Normal University,Chengdu 610068,China;Visual Computing and Virtual Reality Key Laboratory of Sichuan Province,Sichuan Normal University,Chengdu 610068,China)

机构地区：[1]四川师范大学影视与传媒学院,成都610068 [2]四川师范大学可视化计算与虚拟现实四川重点实验室,成都610068

出　　处：《中国图象图形学报》2019年第2期313-323,共11页Journal of Image and Graphics

基　　金：国家自然科学基金项目(81560372);四川省教育厅基金项目(16ZB0069;15ZB0039);可视化计算与虚拟现实四川省重点实验室基金项目(KJ201413)~~

摘　　要：目的决策系统是无人驾驶技术的核心研究之一。已有决策系统存在逻辑不合理、计算效率低、应用场景局限等问题,因此提出一种动态环境下无人驾驶路径决策仿真。方法首先,基于规则模型构建适于无人驾驶决策系统的交通有限状态机;其次,针对交通动态特征,提出基于统计模型的动态目标路径算法计算状态迁移风险;最后,将交通状态机和动态目标路径算法有机结合,设计出一种基于有限状态机的无人驾驶动态目标路径模型,适用于交叉口冲突避免和三车道换道行为。将全速度差连续跟驰模型运用到换道规则中,并基于冲突时间提出动态临界跟车距离。结果为验证模型的有效性和高效性,对交通环境进行虚拟现实建模,模拟交叉口通行和三车道换道行为,分析文中模型对车流量和换道率的影响。实验结果显示,在交叉口通行时,自主车辆不仅可以检测冲突还可以根据风险评估结果执行安全合理的决策。三车道换道时,自主车辆既可以实现紧急让道,也可以通过执行换道达成自身驾驶期望。通过将实测数据和其他两种方法对比,当车流密度在0. 2 0. 5时,本文模型的平均速度最高分别提高32 km/h和22 km/h。当车流密度不超过0. 65时,本文模型的换道成功率最高分别提升37%和25%。结论实验结果说明本文方法不仅可以在动态城区环境下提高决策安全性和正确性,还可以提高车流量饱和度,缓解交通堵塞。Objective Driverless technology is an essential part of intelligent transportation systems,such as environmentalinformation perception,intelligent planning,and multilevel auxiliary driving.This technology reduces driver’s work inten-sification and prevents accidents.With the development of artificial intelligence,autonomous vehicles have attracted con-siderable attention in the industry and academia in recent years.In addition,a decision-making system is a core research ofdriverless technology.The reduction on the number of road accidents is of paramount societal importance,and increasingresearch efforts have been devoted to decision-making systems within the past few years.Conducting human-like decisionswith other encountered vehicles in complex traffic scenarios causes great challenges to autonomous vehicles.The research onautonomous driving decision systems has important theoretical and practical values to improve the level of intelligent vehiclesand intelligent transportation systems.However,the current decision-making system has several limitations,such as unrea-sonable logic,large computational complexity,and limited application scene,due to the uncertainty and randomness of thedriving behavior of surrounding vehicles.To solve these problems,this study constructs a finite-state machine-based deci-sion-making system for the safety driving of autonomous vehicles in dynamic urban traffic environments.This study mainlyinvestigates the passage of vehicles through intersections and their changing of lanes,which are the core issues of decision-making systems.Method The driver’s behavior at a certain period of time is determined based on the current traffic condi-tion and risk perception.We define the primary state of the vehicle based on the driving range of the autonomous vehicle,such as driving at the intersection,driving in the driveway,and approaching the crossroads.Each primary state includesmany secondary states.For example,a vehicle at crossroads may turn or keep straight.Combined with the original fini

关 键 词：风险系数 换道行为 冲突避免 无人驾驶技术 全速度差连续跟驰模型 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]