基于混合模糊多人多目标非合作博弈的车道选择模型

建立汽车安全驾驶辅助系统（包括安全驾驶预警系统）是保证交通安全的有效手段.准确预测车辆集群态势是汽车安全辅助驾驶的前提,车道选择是车辆集群态势发生转移最为根本的原因,也是交通流理论研究的基本内容.以往研究没有综合考虑车辆集群复杂态势下各运动实体特征及其操控者类型,以及多个车道间车辆的冲突对车道选择的影响.为此,本文综合考虑各运动实体特征及其操控者类型,基于混合模糊多人多目标非合作博弈方法,建立城市快速路基本路段上的驾驶员车道选择模型.通过分析各方驾驶员在不同车道选择策略下的收益,确定换道博弈的Nash均衡,得到驾驶员最优车道选择策略.研究结果表明:基于混合模糊多人多目标非合作博弈方法建构的驾驶员车道选择模型,其预测准确率可达到85.2%.     

商用车车道保持辅助系统设计

针对商用车质量大、轴距长、重心高的特性,难以精确建模和系统存在高迟滞等问题,综合考虑车速、车辆方位角、车道曲率、车道线置信度和驾驶员扭矩等信息,设计商用车车道保持辅助(LKA)系统激活策略;基于单点预瞄模型和比例—微分(PD)控制算法,提出一种仿人驾驶转向模型,输出辅助扭矩驱动电子助力转向器,控制助力电机实现车道保持功...     

基于深度学习的驾驶员换道行为预测

车道变换在交通安全中起着至关重要的作用,准确预测驾驶员的车道变换行为可以显著提高驾驶安全性.本文提出了一种基于全连接神经网络和循环神经网络的混合神经网络,用于精准预测车道变换行为.并且提出动态时间窗口,提取包括驾驶员生理数据和车辆运动学数据的车道变换特征.最后,通过真实交通场景下的数据验证了所提出模型的有效性.此外,将所提出的模型与五种其他预测模型进行了比较,结果表明,与其他模型相比,本文所提出的预测模型具有更高的精确率和前瞻时间.     

基于Kalman滤波器的车道保持辅助系统研究

以非线性车辆动力学模型作为系统被控对象,利用Matlab/Simulink软件设计一种基于Kalman滤波算法的车道保持驾驶辅助系统。运用Kalman算法估计车辆行驶状态信息,并利用"预瞄—跟随"驾驶员模型—车辆模型—控制器所组成的驾驶员模型在回路仿真的方式对所设计系统进行验证。结果显示所设计的车道保持辅助系统能有效提高车辆路径跟踪能力。     

复杂车路环境下自动驾驶车辆换道仿真研究

自动驾驶车辆换道决策的算法设计对确保自动驾驶车辆的安全平稳运行至关重要.在现有研究基础上,综合考虑了换道车辆与原车道、目标车道前后多辆车的冲突关系,通过引入车辆侵略系数建立复杂路况下的多人动态博弈模型,以寻找自动驾驶车辆在复杂车路环境下的换道决策以及轨迹线规划的最佳策略.随后,通过NGSIM(Next Generation Simulation)数据探究车辆行驶特性,得到车辆侵略系数的准确分布,搭建仿真环境,给出本模型下自动驾驶车辆在不同路况中的策略选择以及对应的轨迹线示意,并将结果与其它模型求得结果进行比较.根据仿真结果,上述算法有效地完成了自动驾驶车辆在复杂车路环境下换道的关键技术问题,可为自动驾驶车辆的换道决策提供一定的技术指导.     

虚拟驾驶员决策行为模型研究

驾驶决策行为是驾驶行为研究的重要内容.为提高驾驶决策行为建模与仿真的可信度,提出了基于分层的驾驶员决策行为模型.将驾驶决策分为策略层、方法层、行动层和车辆控制层四个层次.重点对驾驶员行动层的决策行为进行实现.使用两点法和PID方法相结合,计算车辆转向角度,使用反应点跟车模型计算车辆纵向行驶速度.通过驾驶员跟车行为仿真,分析了跟车行为模型的稳定性和逼真性,说明了驾驶员决策行为模型的可信性,使驾驶行为模型的输出更加真实.     

一种基于合作博弈论的自动驾驶车道变更模型

提出一个新颖的车道变更模型,采用合作博弈方法激励车辆参与合作.首次将合作博弈理论应用到车道变更领域,设计用于两车变道的纳什讨价还价变道模型,然后扩展为三车的合作博弈变道模型,并求出变道模型的纳什讨价还价解和夏普利值.为了进一步激励车辆参与合作,在收益分配方案中加入支付补偿部分来实现整体收益的可转移性,从而取得模型的解....     

人机共驾智能车辆车道偏离横向辅助控制仿真

车道偏离辅助控制对于提高人机共驾智能车辆行驶安全性具有重要的现实意义,为此进行人机共驾智能车辆车道偏离横向辅助控制仿真研究.对车道线进行识别与跟踪,并采用基于车辆当前位置和基于车辆跨越车道线时间的联合算法进行智能车辆车道偏离判断;建立车辆系统模型和驾驶员模型,并通过基于转向和制动的可拓联合方法实现车辆偏离的横向辅助控制.实验结果表明,所研究方法在转向反应时间、车道偏离持续时间、最大转向角速度与最大横向偏离量四个指标的表现上更好,人机共驾智能车辆车道偏离横向辅助控制效果更佳.     

基于Multi-Agent的多车道交通流的分布式仿真研究

该文以Agent技术为基础,对多车道交通流进行了模拟,并与实际的道路交通流特点进行了比较。首先根据驾驶员实际驾车的特点,建立车辆Agent的基本模型,对车辆Agent换道超车和加速策略进行描述。然后以分布式模型为基础,将多车道交通环境中的车辆运行模拟为Multi-Agent的相互作用。在仿真中,采用了面向Agent的仿真软件Starlogo。从微观上看,所描述的车辆模型能够完成加速和换道过程;从宏观上看,仿真中交通流特点与实际情况是一致的。说明该文的模型符合实际情况,同时也表明了使用Agent技术进行多车道交通流系统仿真的准确性和可行性。     

车道变换决策仿真模型

由于影响车道变换因素的多样性、因素自身变化的随机性和影响因素的难测性,正确反映驾驶员换道行为的不确定性,是微观交通流模拟模型中的难点,也是驾驶员车道变换模型相对于跟驰模型落后的最主要原因之一。从驾驶员心理-物理特性的角度出发,利用层次分析法,对驾驶员决策思维的递阶层次进行量化,建立了基于层次分析的驾驶员车道变换决策模型。经过实测数据验证,该方法用于车道变换决策模型的研究是可行的。     

Lane-change detection based on individual driving style

This paper presents a novel method to detect when a vehicle is changing lanes by considering individual driving styles. Some previous works have focused on the relation between lane changes and driving styles; however, the detection method of lane changes while considering driving styles is still not reported. The proposed method extracts a new feature to appropriately describe driving styles based on drivers' risk taking behavior when following the preceding vehicle. Based on the driving style of the target vehicle, performing a lane change is estimated at each time step. The determined driving style is used as input for the lane-change detection method based on the gap acceptance model, and it is integrated with the detection method based on vehicle movements. Experiments are used to demonstrate that the proposed method considerably improves detection performance.     

A hierarchical fuzzy system for modeling driver’s behavior

The study of human behavior during driving is of primary importance for improving the driver??s security. In this study, we propose a hierarchical driver_vehicle_environment fuzzy system to analyze driver??s behavior under stress conditions on a road. We include climate, road and car conditions in fuzzy modeling. For obtaining fuzzy rules, experts?? opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. The number of fuzzy rules is optimized by Particle Swarm Optimization (PSO) algorithm. Also the frequency of pressing on brake and gas pedals and the number of car??s direction changes are used to determine the driver??s behavior under different conditions. Three different positions are considered for driving and decision making; one position in driving lane and two positions in opposite lane. A fuzzy model called Model 1 is presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. The behaviors of different drivers under two stress conditions are investigated. Also we obtained two other models based on fuzzy rules called Model 2 and Model 3 by using Sugeno fuzzy inference. Model 2 has two linguistic terms and Model 3 has four linguistic terms for estimating the time distances with other cars. The results of three models are compared. The comparative studies have shown that simulation results are in good agreement with the real world situations.     

夜晚车辆异常事件分析

夜晚车道模型是车辆跟踪和车辆行为分析的基础,但是当高速公路或者城市道路光线较暗时,很难通过车道检测的方法来建立车道模型,夜晚车辆快速行驶或相邻帧车辆之间重叠度较低时无法实现准确跟踪。针对此类问题提出了一种基于学习的车道模型建立方法和基于多帧的最佳匹配跟踪方法。首先利用自动多阈值分割方法提取场景中光亮的目标;其次,利用车灯的相关特征移除非车灯光亮区域;接着,利用空间信息把车灯聚类成一个车辆目标,利用多帧的最佳匹配跟踪方法进行跟踪;最后利用车辆跟踪参数与车道模型的融合对夜晚车辆异常事件进行分析。实验结果表明,该算法能够准确地检测出夜晚车辆换道、逆向行驶、交通拥挤、停车等异常事件,并且有很强的鲁棒性。     

考虑前车动态效应的高速跟驰交通流研究

为描述交通中存在的“高速跟驰”现象,在NaSch模型的基础上,考虑了前车的运动特性,并结合驾驶员的驾驶行为差异,建立了考虑前车动态效应的高速跟驰交通流模型（DPM）。通过数值模拟得到了高速跟驰规律,当道路车流密度为0.18时,车道上的高速跟驰率为4.93%;同时,通过分析车辆运动的时空特性,模拟出交通流中自由流、同步流以及宽幅运动阻塞现象;还得出了不同驾驶员占比下的速度-流量-密度的关系;并分析了车辆随时间变化的速度及车头间距波动情况,较NaSch模型有更高的交通稳定性。通过NGSIM数据集及国内实测数据验证了DPM模型的有效性和实用性。     

Does False and Missed Lane Departure Warnings Impact Driving Performances Differently?

Lane Departure Warning Systems (LDWS) are known to be effective at preventing lane departures. The current study aimed to further investigate the influence of LDWS incorrect warnings along with the warning onset on driving performances. Performances were considered during missed warned lane departure episodes, as well as correctly warned lane departure episodes following incorrectly warned (false or/and missed warning) episodes. In the reported experiment, the incorrect warnings order of presentation was manipulated along with the warning onsets (partial and full lane departure). Results pointed out that a missed warning is impairing driving performances during the missed warned situation, whereas a false warning is impairing driving performances for the subsequent lane departure. Moreover, if false warning impairment is magnified by the occurrence of a subsequent missed warning, this multiplicative effect is restricted to the following lane departure only. These findings bring both theoretical and practical insights. From a theoretical perspective, the human–machine cooperation LDWS model was refined by the adjunction of a transitional influence of incorrect warnings on the information processing. From a practical perspective, the recommendation to avoid false warnings as much as missed warnings was made.     

Effects on driving performance of interacting with an in-vehicle music player: A comparison of three interface layout concepts for information presentation

Interface design is an important factor in assessing the potential effects on safety of interacting with an in-vehicle information system while driving. In the current study, the layout of information on a visual display was manipulated to explore its effect on driving performance in the context of music selection. The comparative effects of an auditory–verbal (cognitive) task were also explored. The driving performance of 30 participants was assessed under both baseline and dual task conditions using the Lane Change Test. Concurrent completion of the music selection task with driving resulted in significant impairment to lateral driving performance (mean lane deviation and percentage of correct lane changes) relative to the baseline, and significantly greater mean lane deviation relative to the combined driving and the cognitive task condition. The magnitude of these effects on driving performance was independent of layout concept, although significant differences in subjective workload estimates and performance on the music selection task across layout concepts highlights that potential uncertainty regarding design use as conveyed through layout concept could be disadvantageous. The implications of these results for interface design and safety are discussed.     

基于贝叶斯网络构建RoboSim模型的自动驾驶行为决策

为汽车自动驾驶提供安全高效的自动驾驶行为决策,是汽车自动驾驶领域面临的挑战性问题之一.目前,随着自动驾驶行业的蓬勃发展,工业界与学术界提出了诸多自动驾驶行为决策方法,但由于汽车自动驾驶行为决策受环境不确定因素的影响,决策本身也要求实效性及高安全性,现有的行为决策方法难以完全支撑这些要素.针对以上问题,提出了一种基于贝叶斯网络构建RoboSim模型的自动驾驶行为决策方法.首先,基于领域本体分析自动驾驶场景元素之间的语义关系,并结合LSTM模型预测场景中动态实体的意图,进而为构建贝叶斯网络提供驾驶场景理解信息;然后,通过贝叶斯网络推理特定场景的自动驾驶行为决策,并使用RoboSim模型的状态迁移承载行为决策的动态执行过程,以减少贝叶斯网络推理的冗余操作,提高了决策生成的效率. RoboSim模型具有平台无关、能模拟仿真执行周期的特点,并支持多种形式化的验证技术.为确保行为决策的安全性,使用模型检测工具UPPAAL对RoboSim模型进行验证分析.最后,结合变道超车场景案例,进一步证实所提方法的可行性,为设计安全、高效的自动驾驶行为决策提供了一种可行的途径.     

Modeling and simulation of overtaking behavior involving environment

Overtaking is a complex driving behavior for intelligent vehicles. Current research on modeling overtaking behavior pays little attention on the effect of environment. This paper focuses on the modeling and simulation of the overtaking behavior in virtual reality traffic simulation system involving environment information, such as road geometry and wind. First, an intelligent vehicle model is proposed to better understand environment information and traffic situation. Then, overtaking behavior model is introduced in detail, the lane changing feasibility is analyzed and the fuzzy vehicle controllers considering the road and wind effect are researched. Virtual reality traffic simulation system is designed to realize the simulation of overtaking behavior, with realistic road geometry features. Finally, simulation results show the correctness and the effectiveness of our approach.