Influences of Mixed Traffic Flow and Time Pressure on Mistake-Prone Driving Behaviors among Bus Drivers

Bus safety is a matter of great importance in many developing countries, with driving behaviors among bus drivers identified as a primary factor contributing to accidents. This concern is particularly amplified in mixed traffic flow (MTF) environments with time pressure (TP). However, there is a lack of sufficient research exploring the relationships among these factors. This study consists of two papers that aim to investigate the impact of MTF environments with TP on the driving behaviors of bus drivers. While the first paper focuses on violated driving behaviors, this particular paper delves into mistake-prone driving behaviors (MDB). To collect data on MDB, as well as perceptions of MTF and TP, a questionnaire survey was implemented among bus drivers. Factor analyses were employed to create new measurements for validating MDB in MTF environments. The study utilized partial correlation and linear regression analyses with the Bayesian Model Averaging (BMA) method to explore the relationships between MDB and MTF/TP. The results revealed a modified scale for MDB. Two MTF factors and two TP factors were found to be significantly associated with MDB. A high presence of motorcycles and dangerous interactions among vehicles were not found to be associated with MDB among bus drivers. However, bus drivers who perceived motorcyclists as aggressive, considered road users’ traffic habits as unsafe, and perceived bus routes’ punctuality and organization as very strict were more likely to exhibit MDB. Moreover, the results from the three MDB predictive models demonstrated a positive impact of bus route organization on MDB among bus drivers. The study also examined various relationships between the socio-demographic characteristics of bus drivers and MDB. These findings are of practical significance in developing interventions aimed at reducing MDB among bus drivers operating in MTF environments with TP.

Analysis of Stopping Behavior at Rural T-Intersections Using Naturalistic Driving Study Data

Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic since high speeds on intersection approaches are present which can exacerbate the impact of a crash. Additionally, rural areas are often underserved with EMS services which can further contribute to negative crash outcomes. This paper describes an analysis of driver stopping behavior at rural T-intersections using the SHRP 2 Naturalistic Driving Study data. Type of stop was used as a safety surrogate measure using full/rolling stops compared to non-stops. Time series traces were obtained for 157 drivers at 87 unique intersections resulting in 1277 samples at the stop controlled approach for T-intersections. Roadway (i.e. number of lanes, presence of skew, speed limit, presence of stop bar or other traffic control devices), driver (age, gender, speeding), and environmental characteristics (time of day, presence of rain) were reduced and included as independent variables. Results of a logistic regression model indicated drivers were less likely to stop during the nighttime. However presence of intersection lighting increased the likelihood of full/rolling stops. Presence of intersection skew was shown to negatively impact stopping behavior. Additionally drivers who were traveling over the posted speed limit upstream of the intersection approach were less likely to stop at the approach stop sign.

Virtual Reality Driving Simulation for Measuring Driver Behavior and Characteristics

This article provides new insights regarding driver behavior, techniques and adaptability. This study has been done because: 1) driving a vehicle is critical and one of the most common daily tasks;2) simulators are used for the purpose of training and researching driver behavior and characteristics;3) the article addresses driver experience by involving new virtual reality technologies. A simulator has been used to assist novice drivers to learn how to drive in a very safe environment, and researching and collecting data for researchers has become easier due to this secure and user-friendly environment. The theoretical framework of this driving simulation has been designed by using the Unity3D game engine (5.4.f3 version) and was programmed with the C# programming language. To make the driving environment more realistic we, in addition, utilized the HTC Vive Virtual reality headset which is powered by Steamvr. We used Unity Game Engine to design our scenarios and maps because by doing this we are able to be more flexible with designing. In this study, we asked 10 people ranging from ages 19 - 37 to participate in this experiment. Four Japanese divers and six non-Japanese drivers engaged in this study, some of which do not have a driver’s license in Japan. A few Japanese drivers have a license and car, while others have a license but no car. In order to analyze the results of this experiment we are used MatlabR2016b to analyze the gathered data. The result of this research indicates that individual’s behavior and characteristics such as controlling the speed, remaining calm and relaxed when driving, driving at appropriate speeds depending on changes in road structures and etc. can affect their driving performance.

BEHAVIORAL TOXICOLOGICAL RESEARCHES ON AUTOMOBILE DRIVERS' DRIVING ACCIDENTS

125 automobile drivers, including 85 drivers with accidents (Accident Group, AG) and 40 drivers without accidents (Nonaccident Group, NAG), and 51 controls (Control Group. CG )were tested with WHO Neurobehavioral Core Test Battery (WHO NCTB). The results showed that there were obvious negative mood states such as tension-anxiety and fatigue in AG and drivers with accidents had more poor neurobehavioral performances, especially attention, response speed and perceptual-motor speed than drivers without accidents and controls. We also found that automobile drivers' neurobehavioral functions got weakened with the increase of their age and got strengthened with the elevation of their educational level. And the functions were inversely correlative to the accidents they cuased. The results of our study suggest that WHO NCTB can be an index of researches on driving accidents that automobile drivers caused and can be used in occupational selection and training of drivers.

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Development of a Driving Simulator with Analyzing Driver’s Characteristics Based on a Virtual Reality Head Mounted Display

Driving a vehicle is one of the most common daily yet hazardous tasks. One of the great interests in recent research is to characterize a driver’s behaviors through the use of a driving simulation. Virtual reality technology is now a promising alternative to the conventional driving simulations since it provides a more simple, secure and user-friendly environment for data collection. The driving simulator was used to assist novice drivers in learning how to drive in a very calm environment since the driving is not taking place on an actual road. This paper provides new insights regarding a driver’s behavior, techniques and adaptability within a driving simulation using virtual reality technology. The theoretical framework of this driving simulation has been designed using the Unity3D game engine (5.4.0f3 version) and programmed by the C# programming language. To make the driving simulation environment more realistic, the HTC Vive Virtual reality headset, powered by Steamvr, was used. 10 volunteers ranging from ages 19 - 37 participated in the virtual reality driving experiment. Matlab R2016b was used to analyze the data obtained from experiment. This research results are crucial for training drivers and obtaining insight on a driver’s behavior and characteristics. We have gathered diverse results for 10 drivers with different characteristics to be discussed in this study. Driving simulations are not easy to use for some users due to motion sickness, difficulties in adopting to a virtual environment. Furthermore, results of this study clearly show the performance of drivers is closely associated with individual’s behavior and adaptability to the driving simulator. Based on our findings, it can be said that with a VR-HMD (Virtual Reality-Head Mounted Display) Driving Simulator enables us to evaluate a driver’s “performance error”, “recognition errors” and “decision error”. All of which will allow researchers and further studies to potentially establish a method to increase driver safety or alleviate “driving errors”.

A new cellular automaton for signal controlled traffic flow based on driving behaviors

The complexity of signal controlled traffic largely stems from the various driving behaviors developed in response to the traffic signal. However, the existing models take a few driving behaviors into account and consequently the traffic dynamics has not been completely explored. Therefore, a new cellular automaton model, which incorporates the driving behaviors typically manifesting during the different stages when the vehicles are moving toward a traffic light, is proposed in this paper. Numerical simulations have demonstrated that the proposed model can produce the spontaneous traffic breakdown and the dissolution of the over-saturated traffic phenomena. Furthermore, the simulation results indicate that the slow-to-start behavior and the inch-forward behavior can foster the traffic breakdown. Particularly, it has been discovered that the over-saturated traffic can be revised to be an under-saturated state when the slow-down behavior is activated after the spontaneous breakdown. Finally, the contributions of the driving behaviors on the traffic breakdown have been examined.

Tensioner Vibration and Belt Slipping Behavior Investigation for Damped Serpentine Belt Drive Systems

A nonlinear rotational motion model for n-pulley damped serpentine belt drive systems （SBDSs） was developed.The effects of the belt deflection along the contact arc of pulleys on the belt span tensions were considered.The methods for calculating the tensioner arm vibration and belt slipping on pulleys were introduced.The effects of belt damping on rotational vibration of tensioner arm and belt slipping on pulleys were studied.Numerical solutions for a 3-pulley SBDS indicate that the belt slipping at the steady states should be controlled to avoid belt slipping at transient states.The slip factors tend to decrease when the belt damping increases,and the possibility of the belt slipping can be controlled through adjusting the wrap angles of pulleys and the preload of the tensioner when the design parameters of SBDS remain constant.

Abnormal driving behavior identification based on direction and position offsets

Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reliability under severe traffic scenes. This paper proposes a new ADBI method based on direction and position offsets,where a two-factor identification strategy is proposed to improve the accuracy and reliability of ADBI. Self-adaptive edge detection based on Sobel operator is used to extract edge information of lanes. In order to enhance the efficiency and reliability of lane detection,an improved lane detection algorithm is proposed,where a Hough transform based on local search scope is employed to quickly detect the lane,and a validation scheme based on priori information is proposed to further verify the detected lane. Experimental results under various complex road conditions demonstrate the validity of the proposed ADBI.

Bifurcation analysis of visual angle model with anticipated time and stabilizing driving behavior

In the light of the visual angle model(VAM),an improved car-following model considering driver's visual angle,anticipated time and stabilizing driving behavior is proposed so as to investigate how the driver's behavior factors affect the stability of the traffic flow.Based on the model,linear stability analysis is performed together with bifurcation analysis,whose corresponding stability condition is highly fit to the results of the linear analysis.Furthermore,the time-dependent Ginzburg–Landau(TDGL)equation and the modified Korteweg–de Vries(m Kd V)equation are derived by nonlinear analysis,and we obtain the relationship of the two equations through the comparison.Finally,parameter calibration and numerical simulation are conducted to verify the validity of the theoretical analysis,whose results are highly consistent with the theoretical analysis.

Power-Law Distributions in Hard Drive Behavior

Taking into account the fact that the computer systems, as the implementations of Turing machine, are physical devices, the paper shows considerations in which hard drive behavior will be presented in terms of statistical mechanics. Because computer is a machine, its analysis cannot be based only on mathematical models apart of physical conditions. In the paper it will be presented a very narrow part this problem – an analysis of hard drive behavior in the context of the power-law distributions. We will focus only on four selected hard drive parameters, i.e. the rate of transfer bytes to or from the disk during the read or write, the number of pending requests to the disk and the rate of read operations. Our research was performed under the Windows operating system and this allows to make a statistical analysis for the possible occurrence of power-laws representing the lack of characteristic scale for considered processes. This property will be confirmed in all analyzed cases. A presented study can help describing the behavior of the whole computer system in terms of physics of computer processing.

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.

基于实车路试的高速公路减速标线运行效果研究

为探寻高速公路减速标线设置对小客车的纵向运行特性的影响,明确减速标线的减速效果,以G50沪渝高速重庆段的3处减速标线区段为对象,开展了实车驾驶试验,采集了自然驾驶状态下的车辆速度、加速度和轨迹等数据,对比分析了车辆经过3处减速标线区段时的速度整体特征、幅值分布和变化模式;同时分析了纵向减速度变化特征;根据速度与加速度的组合示意图和纵向减速标线设置区段内速度的最大降幅、起止点的速度差异性,评价了减速标线的实施效果。结果表明:由于道路线形条件不同,3处减速标线区段的车辆行驶速度整体特征和变化模式存在差异,其中弯坡组合路段车辆速度存在波动,分为“近匀速—减速—加速—稳定—加速”的五阶段变化模式;减速度中位值和85%位值分别为0.123 m/s^(2)、0.225 m/s^(2)、0.185 m/s^(2)和0.346 m/s^(2)、0.513 m/s^(2)、0.489 m/s^(2)。减速标线主要有两方面作用效果:能够使车辆运行车速显著降低,或能够抑制车辆速度增加的趋势。研究成果为高速公路减速标线有效性评价及安全改善提供了基础数据支持。

山区公路交叉口驾驶避险决策行为特性分析

为研究驾驶特征指标与驾驶避险行为决策的关联规则以提升驾驶安全,将驾驶避险决策行为划分为纵向“停车避险”和横向“转向避险”,并运用驾驶模拟系统构建12种山区公路交叉口交通冲突虚拟场景,招募38名驾驶人进行试验,采集车辆特征和驾驶人扫视、眨眼、注视等眼动特征数据。基于随机森林构建驾驶避险决策行为模型,然后引入沙普利加和解释(SHapley Additive exPlanation, SHAP)框架进一步分析车辆、眼动特征与驾驶避险行为之间的非线性关系。结果表明:模型对纵、横向避险行为预测的准确率分别为84.77%、94.70%;纵向速度标准差、扫视持续时间标准差、轨迹偏差标准差、侧向速度标准差与驾驶避险决策行为存在明显关联,如纵向速度标准差过大(约大于10 km/h),纵向“停车避险”可能性明显增加。

驾驶行为分类方法及量化评估综述

对驾驶行为分类方法及量化评估进行综述,首先阐述了驾驶行为的涵义及表征方法,然后将驾驶行为分类方法划分为基于统计特性的分类方法、基于机器学习的分类方法、混合式(组合、集成)分类方法三大类,并对不同的驾驶行为分类方法的代表算法、优点及局限性等进行系统归纳和总结,其次,从多个维度对驾驶行为的量化评估研究进行系统介绍,最后介绍了驾驶行为分类及量化评估研究成果在多个领域的应用现状及前景展望。

无信控交叉口驾驶人潜在危险感知能力评估

为量化无信控交叉口驾驶人潜在危险的感知能力,组织驾驶人实车驾驶试验,实时采集驾驶人眼动特征参数。解析熟练和非熟练驾驶人直行通过无信控交叉口模式下的扫视和注视规律,引入马尔科夫链模型,阐释驾驶人注视转移特性,揭示驾驶人视觉搜索与潜在危险感知特性的内在联系。结合熟练和非熟练驾驶人各映射指标下的统计学分析,萃取驾驶人潜在危险感知能力表征参数。基于灰色近优综合评价法,构建驾驶人在无信控交叉口场景下的潜在危险感知能力评估模型。研究表明:直行条件下,无信控交叉口下非熟练驾驶人的水平搜索广度、垂直搜索深度和扫视强度显著低于熟练驾驶人;相较于熟练驾驶人,非熟练驾驶人兼顾道路两侧信息的能力较弱,且注视概率的适时调配机制不够灵活;熟练驾驶人在无信控交叉口的潜在危险感知得分比非熟练驾驶人高31.2%;熟练驾驶人中,男性潜在危险感知能力明显强于女性,而性别对非熟练驾驶人感知绩效无显著影响。研究结果可丰富防御性驾驶相关理论体系,为无信控交叉口交通设施的优化和完善,驾驶人安全教育及评估等提供重要参考。

基于深度强化学习的无信号灯路口决策研究

无信号灯左转路口是自动驾驶场景中最为危险的场景之一,如何实现高效安全的左转决策是自动驾驶领域的重大难题。深度强化学习(DRL)算法在自动驾驶决策领域具有广阔应用前景。但是,深度强化学习在自动驾驶场景中存在样本效率低、奖励函数设计困难等问题。提出一种基于专家先验的深度强化学习算法(CBAMBC SAC)来解决上述问题。首先,利用SMARTS仿真平台获得专家先验知识;然后,使用通道-空间注意力机制(CBAM)改进行为克隆(BC)方法,在专家先验知识的基础上预训练模仿专家策略;最后,使用模仿专家策略指导深度强化学习算法的学习过程,并在无信号灯路口左转决策中进行验证。实验结果表明,基于专家先验的DRL算法比传统的DRL算法更具优势,不仅可以免去人为设置奖励函数的工作量,而且可以显著提高样本效率从而获得更优性能。在无信号灯路口左转场景下,CBAM-BC SAC算法与传统DRL算法(SAC)、基于传统行为克隆的DRL算法(BC SAC)相比,平均通行成功率分别提高了14.2和2.2个百分点。

基于尾灯灯语的混行交通流车辆驾驶意图识别模型研究

针对长期存在自动驾驶车辆(autonomous vehicle,AV)与人工驾驶车辆(human-driven vehicle,HV)混行的交通现状,结合深度学习和HSV颜色特征提取方法,提出了一种在混行交通环境下基于尾灯灯语的车辆驾驶意图识别模型。以Mask R-CNN(mask region proposal convolutional neural network)车辆模型检测出的感兴趣区域RoI(region of interest)为限制,将区域内的HV作为研究对象,根据尾灯位置相关性,在HV车尾区域添加纵横向约束来定位传递灯语信号的左右尾灯;在规定的灯语组合及转向灯闪烁频率基础上,提出了一种基于时间序列的灯语识别算法,运用多目标判别相关性滤波CSRT(channel and spatial relatability tracking)跟踪HV尾灯并统计尾灯时序状态;以动态灯语作为输入,构建基于尾灯灯语的驾驶意图识别模型;使用基于真实路况信息的Cityspaces数据集和交通流视频数据对模型进行训练、验证和测试。研究结果表明:基于尾灯灯语的驾驶意图识别模型对视频流车辆尾灯检测准确率和召回率分别为96.0%、98.2%,对驾驶意图识别的平均准确率达到了95.9%,单帧识别耗时为20 ms,为高速混行环境下的AV决策规划提供了有效的理论依据。