Optical information for car following: the driving by visual angle (DVA) model

OBJECTIVE: The present study developed and tested a model of car following by human drivers. BACKGROUND: Previous models of car following are based on 3-D parameters such as lead vehicle speed and distance information, which are not directly available to a driver. In the present paper we present the driving by visual angle (DVA) model, which is based on the visual information (visual angle and rate of change of visual angle) available to the driver. METHOD: Two experiments in a driving simulator examined car-following performance in response to speed variations of a lead vehicle defined by a sum of sine wave oscillations and ramp acceleration functions. In addition, the model was applied to six driving events using real world-driving data. RESULTS: The model provided a good fit to car-following performance in the driving simulation studies as well as in real-world driving performance. A comparison with the advanced interactive microscopic simulator for urban and nonurban networks (AIMSUN) model, which is based on 3-D parameters, suggests that the DVA was more predictive of driver behavior in matching lead vehicle speed and distance headway. CONCLUSION: Car-following behavior can be modeled using only visual information to the driver and can produce performance more predictive of driver performance than models based on 3-D (speed or distance) information. APPLICATION: The DVA model has applications to several traffic safety issues, including automated driving systems and traffic flow models.     

考虑后视效应和多前车信息的跟驰模型

网联车跟驰模型的研究可为未来实施大规模的实地测试提供模型参考,已成为交通流及智能交通领域的研究热点.为了更好地研究智能网联车的跟驰特性,在MVD模型的基础上,提出了一种考虑后视效应和多前车信息的跟驰模型(BL-MVDAM),利用线性稳定性分析方法推导出BL-MVDAM模型的交通流稳定性判断依据,并分别分析了模型中各参数对系统稳定性的影响,给出分析结果并进行了数值仿真实验.仿真实验选取在环形道路上给行驶过程中的车队施加轻微扰动,并根据跟驰车对后车的关注程度P和前车数量k设计数值模拟实验,当其他条件一致时,本文模型相比FVD,MVD,OMVC和BLVD模型,BL-MVDAM模型中车队的速度波动率较小,尤其是当P=0.8,k=3 时,车队速度平均波动率最小可以达0.24％,实验分析结果表明,所提出模型在引入后视效应和多前车信息后,具备更优的稳定区域,能较好地吸收扰动且有利于增强车队行驶的稳定性.     

Predictive car-following scheme for improving traffic flows on urban road networks

Driving behavior is one of the main reasons that causes bottleneck on the freeway or restricts the capacity of signalized intersections. This paper proposes a car-following scheme in a model predictive control (MPC) framework to improve the traffic flow behavior, particularly in stopping and speeding up of individual vehicles in dense urban traffic under a connected vehicle (CV) environment. Using information received through vehicle-to-vehicle (V2V) communication, the scheme predicts the future states of the preceding vehicle and computes the control input by solving a constrained optimization problem considering a finite future horizon. The objective function is to minimize the weighted costs due to speed deviation, control input, and unsafe gaps. The scheme shares the planned driving information with the following vehicles so that they can make better cooperative driving decision. The proposed car-following scheme is simulated in a typical driving scenario with multiple vehicles in dense traffic that has to stop at red signals in multiple intersections. The speeding up or queue clearing and stopping characteristics of the traffic using the proposed scheme is compared with the existing car-following scheme through numerical simulation.     

虚拟自主车智能驾驶行为的研究与实现

为了提高驾驶模拟系统的逼真度和可信度,以自主车驾驶行为作为研究对象,在结合宏观与微观信息进行虚拟道路环境建模的基础上,提出一种基于局部信息感知和知识随机决策的概率模拟方法,目的是提升虚拟车的自主智能性和实现驾驶行为的真实随机性。实验与应用结果表明,提出的方法能够较真实地再现车辆的自主驾驶行为,模拟现实中交通车辆环境,并提高了模拟系统的真实性、形象性和实时性。     

考虑车辆协同与时间延迟的跟驰模型的弱非线性分析

为解决交通系统中合作驾驶引起的交通流问题,提出一种广义力跟驰模型。采用线性稳定性理论分析法推导出模型的线性稳定性条件并分析车辆协同与时滞组合对交通流稳定性的影响。通过非线性分析方法推导出考虑合作与时滞的车辆跟驰模型的Burgers方程与KdV方程,并给出它们的孤立波解及约束条件。利用解析法与数值分析法研究交通波演化过程中合作最优速度(OV)模型。实验结果表明,合作与延迟是依赖于模型的,车辆协同作用有利于抑制交通阻塞,传感器对延迟车辆的延迟检测在一定程度上有利于缓解失稳效应。     

考虑时延的智能网联汽车混合交通流稳定性分析

为研究时延对交通流稳定性的影响,构建考虑时延的人工驾驶汽车和智能网联汽车混合交通流稳定性分析模型.首先,分析并确定混合交通流中不同类型跟驰模式的比例关系和时延取值;然后,在此基础上采用不同的跟驰参数和时延值区分车辆的跟驰模式,并由此推导出混合交通流线性稳定条件;最后,以智能驾驶员模型为例,通过设计数值实验分别讨论智能网...     

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。     

跟车风险的概率估计方法研究

首次将追尾事故发生的过程分为前车减速与前车减速条件下的碰撞两起事件,运用条件概率的思想求出追尾事故发生的概率来表征跟车风险。首先从安全的本质出发提出了跟车风险的表述方法;再从实际道路采集减速度数据,根据实际数据建立密度函数模型。在分析前车减速条件下追尾碰撞条件时,从汽车地面力学理论的角度补充考虑了制动系作用时间、附着系数等影响因素,使模型更接近实际状况,并且将判断的标准由停车距离扩展到了制动全过程的位移。最后,运用视频检测方法提取杭州市道路交通运行数据对估计方法进行了试算。     

基于心理物理综合认知结构的微观交通仿真模型

针对驾驶行为的不确定性,在分析驾驶员心理-物理微观特性的基础上,构建了基于驾驶员任务集聚的心理-物理综合认知结构及其各行为运行模式下微观交通仿真的车辆跟驰模型.运用五轮仪试验系统所采获的实际数据和多元统计分析的数学方法对模型进行了标定,并使用与模型标定过程所用到的数据不同的另一部分数据验证了模型的有效性.结果表明,该文所提出的模型和算法能够很好地刻画驾驶员心理-物理行为特性的复杂性,再现人车单元的实际动态行为,为网络交通流一体化协同仿真和智能运输系统研究提供理论基础.     

基于射频识别技术的智能交通系统

基于射频识别技术的一些新特点，提出了一套城市智能交通管理系统模型，用来解决城市交通管理中的一系列经典难题．该模型结合射频识别技术、数字图像处理、网络通信以及数据库技术，能够实时监控交通流量、车辆信息以及交通状况；再结合状态空间穷举法和信息融合技术，可以实现智能交通调控、自动违章处理、车辆跟踪处理、交通实时查询以及车辆统计等功能．     

面向智能驾驶测试的仿真场景构建技术综述

随着汽车智能化程度的不断提高,智能汽车通过环境传感器与周边行驶环境的信息交互与互联更为密切,需应对的行驶环境状况也越来越复杂,包括行驶道路、周边交通和气象条件等诸多因素,具有较强的不确定性、难以重复、不可预测和不可穷尽。限于研发周期和成本、工况复杂多样性,特别是安全因素的考虑,传统的开放道路测试试验或基于封闭试验场的测试难以满足智能驾驶系统可靠性与鲁棒性的测试要求。因此,借助数字虚拟技术的仿真测试成为智能驾驶测试验证一种新的手段,仿真场景的构建作为模拟仿真的重要组成部分,是实现智能驾驶测试中大样本、极限边界小概率样本测试验证的关键技术,这对提升智能驾驶系统的压力和加速测评水平显得尤为重要。面向智能驾驶测试的仿真场景构建技术已成为当前汽车智能化新的研究课题和世界性的研究热点,作为一种新兴技术仍面临许多挑战。本文提出了面向智能驾驶测试的仿真场景构建方法,系统阐述了国内外研究工作的进展与现状,包括场景自动构建方法和交通仿真建模方法,重点分析一些值得深入研究的问题并围绕场景构建技术的发展趋势进行了讨论分析,最后介绍了团队相关研究在2020中国智能驾驶挑战赛仿真赛和世界智能驾驶挑战赛的仿真场景应用情况。     

高速公路合流区车间无线通讯安全辅助驾驶研究

针对高速公路入口匝道合流处易发生交通事故,而传统汽车安全辅助驾驶系统在这类区域无法实际应用等问题,提出了利用车间无线网络通讯、GPS与电子地图匹配等技术协同来解决这些问题。并采用基于前后车辆间跟驰模型的加速度风险评价标准作为安全评价,构建系统并在校园内进行了简单的道路实验。实验证明提出的方法可有效地解决传统汽车安全辅助驾驶系统中各类传感器失效而造成的问题,并可应用于极端的气候条件、特殊的路况包括交叉路口等复杂道路下的安全辅助驾驶等各类系统。     

Model predictive control for hybrid vehicle ecological driving using traffic signal and road slope information

This paper presents development of a control system for ecological driving of a hybrid vehicle. Prediction using traffic signal and road slope information is considered to improve the fuel economy. It is assumed that the automobile receives traffic signal information from intelligent transportation systems(ITS). Model predictive control is used to calculate optimal vehicle control inputs using traffic signal and road slope information. The performance of the proposed method was analyzed through computer simulation results. Both the fuel economy and the driving profile are optimized using the proposed approach. It was observed that fuel economy was improved compared with driving of a typical human driving model.     

车路协同路侧感知融合方法的研究

随着智能网联汽车技术和产业的不断发展,智能网联汽车逐渐成为人们交通出行的选择之一。但受智能网联汽车自身环境感知系统对特定道路交通场景信息处理的局限,无法实现在所有行驶工况下安全高效的运行,其需车路协同路侧感知技术的辅助方能更安全高效的运行。海量的车路协同感知数据是城市道路和高速公路车路协同、运行分析和科学管理的宝藏,理解和分析这些数据是车路协同路侧感知融合的关键。面对车路协同路侧多传感器的不同数据,如何高效准确地挖掘和提取雷达、视频在不同时间、不同空间维度的数据,实现对重点交通场景(如视野盲区、急转弯道、隧道、桥梁)和交通事件、环境、设施安全等的雷达、视频数据进行快速融合检测、识别与检索,通过蜂窝车联网C-V2X网络在一定时延范围内有效地将路侧感知融合结果数据发送给智能网联汽车,确保其安全高效的行驶,是面向智能网联汽车辅助驾驶的车路协同路侧感知融合的关键问题。基于智能网联汽车其自身环境感知能力,对道路智能基础设施感知网络中的多传感器融合方法进行研究分析,提出了基于误差方差的多传感器融合算法,与非智能道路相比,其效率更高,更加智能化,可有效解决道路交通运行环境中存在的常见问题,为人们提供更加安全、高效、优质的交通出行服务。     

结合状态机和动态目标路径的无人驾驶决策仿真

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0.20.5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。     

面向智能驾驶的平行视觉感知：基本概念、框架与应用

目的 视觉感知技术是智能车系统中的一项关键技术,但是在复杂挑战下如何有效提高视觉性能已经成为智能驾驶领域的重要研究内容。本文将人工社会（artificial societies）、计算实验（computational experiments）和平行执行（parallel execution）构成的ACP方法引入智能驾驶的视觉感知领域,提出了面向智能驾驶的平行视觉感知,解决了视觉模型合理训练和评估问题,有助于智能驾驶进一步走向实际应用。方法 平行视觉感知通过人工子系统组合来模拟实际驾驶场景,构建人工驾驶场景使之成为智能车视觉感知的“计算实验室”;借助计算实验两种操作模式完成视觉模型训练与评估;最后采用平行执行动态优化视觉模型,保障智能驾驶对复杂挑战的感知与理解长期有效。结果 实验表明,目标检测的训练阶段虚实混合数据最高精度可达60.9%,比单纯用KPC（包括：KITTI（Karlsruhe Institute of Technology and Toyota Technological Institute）,PASCAL VOC（pattern analysis,statistical modelling and computational learning visual object classes）和MS COCO（Microsoft common objects in context））数据和虚拟数据分别高出17.9%和5.3%;在评估阶段相较于基准数据,常规任务（-30°且垂直移动）平均精度下降11.3%,环境任务（雾天）平均精度下降21.0%,困难任务（所有挑战）平均精度下降33.7%。结论 本文为智能驾驶设计和实施了在实际驾驶场景难以甚至无法进行的视觉计算实验,对复杂视觉挑战进行分析和评估,具备加强智能车在行驶过程中感知和理解周围场景的意义。     

考虑后视和多前车信息反馈的车辆跟驰模型

为改进车联网环境下车辆跟驰模型的稳定性,在经典OVCM模型基础上考虑后视效应、多前车速度差和多前车最优速度记忆综合信息对交通流稳定性能的影响,提出一种基于后视和多前车信息反馈的扩展车辆跟驰模型。根据线性稳定性分析法得出模型的中性稳定性判断条件,并进行数值仿真实验与分析。实验结果表明,在扰动初始条件设置一致下,所提模型相比于OV、FVD、OVCM模型,交通流稳定区域增大,速度波动幅度减小,特别是考虑的前车数k、后视敏感系数λi和记忆效应敏感系数γi取值为k=3,λi=［0.2,0.15,0.1］,γi=［0.1,0.08,0.06］时,车辆的平均速度波动率低于0.1%,由此说明,所提模型能有效减少扰动影响,增强交通流的稳态保持。     

Classify vehicles in traffic scene images with deformable part-based models

Vehicle classification is an important and challenging task in intelligent transportation systems, which has a wide range of applications. In this paper, we propose to integrate vehicle detection and vehicle classification into one single framework by using deformable part-based models. First of all, we use annotated vehicle images to train a deformable part-based model for each class of vehicles to be classified. Then, given a traffic scene image, we employ the obtained vehicle models to perform vehicle detection in it for vehicle extraction. After that, model alignment is performed on the extracted image crop, based on which features are extracted for creating a representation for the vehicle in the given image. We train a linear multi-class Support Vector Machine classifier based on representations of images in a validation set. Finally, we adopt the SVM classifier for vehicle classification. The proposed method is evaluated on the BIT-Vehicle Dataset, and can achieve an accuracy of \(91.08\%\), which is superior to methods used for comparison. Obtained results demonstrated the effectiveness of the proposed method.     

Round-trip driving effects on driving performances and mental workload under different traffic rules

This study investigates the effect of switching between different traffic rules (left-versus right-hand traffic) on driving performance and mental workload. A driving simulation environment was developed according to the real environment. Two urban roads with different traffic systems were simulated. Twenty participants executed intersection turns and continuous car-following behavior in four simulated driving stages, including driving with familiar, unfamiliar, second time unfamiliar, and back to familiar traffic rules. The mean and standard deviations for speed, distance headway, and the standard deviation of lateral position were recorded as driving performance. Mental workload was determined using the NASA-TLX and Rating Scale Mental Effort questionnaires. One-way analysis of variance was used to evaluate the differences between the four driving stages using subjective and objective measures. The results showed that significant differences were obtained in all measures when driving in the four driving situations, except for the speed standard deviation. The car-following behavior was the most unsafe (significantly larger standard deviations for distance headway and mental workload) when driving in unfamiliar road traffic compared with the other stages. When driving under unfamiliar traffic rules for the second time, the mental workload was significantly relieved and the lane-keeping ability significantly improved. The results indicated that providing an adaptive runway for drivers to familiarize themselves with different traffic rules is necessary to improve driving performance and safety. These findings provide useful information for designing bridges linking two places with different traffic rules to increase traffic safety.     

Microscopic car-following model for the traffic flow: the state of the art

Car-following theory is an important research direction in the field of intelligent transportation systems (ITS), it describes the one-by-one following process of vehicles on the same lane in traffic flow, and one of its important issues is congestion control. To explore the strategy for controlling traffic congestion, this paper introduces and analyzes some classic car-following models, and gives a systematic review of their developments. Moreover, in order to introduce the approach to analyze the stability, taking the full velocity difference (FVD) model for example, the local and asymptotic stability analysis is discussed, while the corresponding nonlinear analysis is also conducted. Then, some perspectives of the car-following model are given in the final.