Continual driver behaviour learning for connected vehicles and intelligent transportation systems: Framework, survey and challenges

Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep learning(DL),numerous driver behaviour learning(DBL)methods have been proposed and applied in connected vehicles(CV)and intelligent transportation systems(ITS).This study provides a review of DBL,which mainly focuses on typical applications in CV and ITS.First,a comprehensive review of the state-of-the-art DBL is presented.Next,Given the constantly changing nature of real driving scenarios,most existing learning-based models may suffer from the so-called“catastrophic forgetting,”which refers to their inability to perform well in previously learned scenarios after acquiring new ones.As a solution to the aforementioned issue,this paper presents a framework for continual driver behaviour learning(CDBL)by leveraging continual learning technology.The proposed CDBL framework is demonstrated to outperform existing methods in behaviour prediction through a case study.Finally,future works,potential challenges and emerging trends in this area are highlighted.

Driver Steering Behaviour Modelling Based on Neuromuscular Dynamics and Multi‑Task Time‑Series Transformer

Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle.In this study,a multi-task sequential learning framework is developed to pre-dict future steering torques and steering postures based on upper limb neuromuscular electromyography signals.The joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering behaviours.Regarding different testing scenarios,two driving modes,namely,both-hand and single-right-hand modes,are studied.For each driving mode,three different driving postures are further evaluated.Next,a multi-task time-series transformer network(MTS-Trans)is developed to predict the future steering torques and driving postures based on the multi-variate sequential input and the self-attention mechanism.To evaluate the multi-task learning performance and information-sharing characteristics within the network,four distinct two-branch network architectures are evaluated.Empirical validation is conducted through a driving simulator-based experiment,encompassing 21 participants.The pro-posed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both two-hand and single-hand driving modes.These findings hold significant promise for the advancement of driver steering assistance systems,fostering mutual comprehension and synergy between human drivers and intelligent vehicles.

Driver Management and Service Delivery: Insights within State-Owned Enterprises in Zimbabwe

Driver management may be considered an organization’s most valuable asset, and State-Owned Enterprises (SOEs) must invest in it to secure their survival and growth. The study sought to establish the effect of driver management on service delivery in SOEs. Various empirical studies reveal that there is a lack of understanding of the impact of driver management on service delivery in public organizations, notably SOEs, resulting in sectorial and contextual research gaps that must be filled. The study used a mixed-method research strategy and a pragmatic research philosophy. In addition, 344 respondents from 86 SOEs were given standardized questionnaires to complete. The researchers employed stratified and purposive sampling. Statistical package for social scientists (SPSS) version 20 was used to generate descriptive statistics. All study items were subjected to exploratory factor analysis (EFA), and research hypotheses were assessed using Structural Equation Modelling (SEM) in AMOS version 21. The study findings revealed that despite having clear driver recruiting procedures, there is bad driver conduct and a lack of driver recognition programs to reward good driving. The study concluded that driver management has a positive effect on service delivery. In light of these conclusions, the study suggests that SOEs should ensure that drivers understand their responsibilities when using company vehicles. Furthermore, drivers should be periodically trained in line with the tenets of the New Public Management Theory which advocates for quality service delivery, customer centrism and reduction in rigidity.

Exploring Challenges in Developing a Smart and Effective Assistive System for Improving the Experience of the Elderly Drivers

As the overall population ages, driving-related accidents and injuries, associated with elderly drivers, have risen. Existing research about elderly drivers mainly focuses on factual data collection and analysis, indicating the elderly＇s growing fatal accident rates and their different behaviours compared to younger drivers. However, few research has focused on design-led practical solutions to mitigate the elderly＇s growing fatal accidents, by consid- ering their usability and body conditions, afflicting the elderly, such as decreased vision, hearing, and reaction times. In this paper, first, current worldwide situations on growing fatal accident rates for elderly drivers is reviewed and the key impact factors are identified and discussed with regarding to usability and design trend in the automotive technology for elderly. Second, existing smart vehicle technology-based solutions to promote safe driving are explored and their pros and cons are discussed and anal- ysed. Most of solutions are not created by people with driving difficulties, which are caused by health problems most commonly afflicting the elderly. Thirdly, diverse design-led research activities are taken, such as a survey, observation, and interviews to gain new understanding of what kinds of driving problems elderly drivers have and demonstrate how new system concepts could be developed for the elderly＇s benefits. Finally, it is found that the elderly＇s low vision and late reaction are main factors causing their driving difficulties. Based on this finding, usable vehicle system design ideas have been proposed, by utilising facial expression sensing technology as a solution. The proposed solutions would ensure reducing both the elderly＇s driving problems and high fatal accident rates and provide a more enjoyable driving environment for the elderly population.

Extending the decision-making process during yellow phase from human drivers to autonomous vehicles:A microsimulation study with safety considerations

One of the main factors affecting the safety of signalised intersections is the stop/go behaviour during the yellow interval.Although previous research has exhaustively examined drivers’stop/go decision-making,the expected autonomous vehicles’(AVs’)stop/go behaviour has not yet been thoroughly investigated.Through a series of simulation experiments developed for conventional and autonomous vehicles using different carfollowing,lane-changing,lateral placement and stop/go model parameter values,we examine here whether the default VISSIM stop/go parameter values can adequately replicate the observed drivers’behaviour at the considered intersection and assess the suitability of using the currently available options,albeit referring to human drivers,to simulate the expected stop/go behaviour of AVs.We also propose a policy framework for determining the desired behaviour of AVs in yellow interval,which is integrated into an AVs logic and achieved in the last simulation to explore the effect of automation on the stop/go outcome and,hence,on the safety level of signalised intersections.Several data analysis and modeling techniques were used for the formulation of certain scenarios,including binary choice models.The default stop/go parameter values were found unfit to replicate the observed stop/go behaviour and subjected to calibration.Compared to the currently available options,the proposed AVs logic proved to produce the most accurate results,in terms of the stop/go simulation outcome.Regarding the impact of automation on the stop/go outcome,the simulation experiments showed that AVs preferred a more conservative behaviour in favor of road safety,as indicated by the significant reduction(≈15%)in the number of vehicles crossing the stop line during the yellow light and zero instances of red light violation.However,compared to the conservative drivers represented by the default stop/go parameter values,AVs preferred a more rational behaviour in favor of intersection capacity without compromising road safety.

Intelligent speed adaptation for visibility technology affects drivers’speed selection along curves with sight limitations

Sight obstructions along road curves can lead to a crash if the driver is not able to stop the vehicle in time.This is a particular issue along curves with limited available sight,where speed management is necessary to avoid unsafe situations(e.g.,driving off the road or invading the other traffic lane).To solve this issue,we proposed a novel intelligent speed adaptation(ISA)system for visibility,called V-ISA(intelligent speed adaptation for visibility).It estimates the real-time safe speed limits based on the prevailing sight conditions.V-ISA comes with three variants with specific feedback modalities(1)visual and(2)auditory information,and(3)direct intervention to assume control over the vehicle speed.Here,we investigated the efficiency of each of the three V-ISA variants on driving speed choice and lateral behavioural response along road curves with limited and unsafe available sight distances,using a driving simulator.We also considered curve road geometry(curve direction:rightward vs.leftward).Sixty active drivers were recruited for the study.While half of them(experimental group)tested the three V-ISA variants(and a V-ISA off condition),the other half always drove with the V-ISA off(validation group).We used a linear mixed-effect model to evaluate the influence of V-ISA on driver behaviour.All V-ISA variants were efficient at reducing speeds at entrance points,with no discernible negative impact on driver lateral behaviour.On rightward curves,the V-ISA intervening variant appeared to be the most effective at adapting to sight limitations.Results of the current study implies that V-ISA might assist drivers to adjust their operating speed as per prevailing sight conditions and,consequently,establishes safer driving conditions.

信号控制交叉口绿灯间隔时间计算方法

为了解决目前实际应用中因绿灯间隔时间设置不当而产生的安全隐患问题,通过分析机动车的制动特性以及驾驶员的驾驶行为,结合交叉口的实际情况,分别给出了未安装交通信号倒计时装置与安装了交通信号倒计时装置这两种情况下信号控制交叉口的绿灯间隔时间计算公式,以确保两相邻相位通过信号控制交叉口的冲突车辆能够获得合理的绿灯间隔时间并安全顺利地通过交叉口.

Shared Control of Highly Automated Vehicles Using Steer-By-Wire Systems

A shared control of highly automated Steer-by-Wire system is proposed for cooperative driving between the driver and vehicle in the face of driver's abnormal driving. A fault detection scheme is designed to detect the abnormal driving behaviour and transfer the control of the car to the automatic system designed based on a fault tolerant model predictive control(MPC) controller driving the vehicle along an optimal safe path.The proposed concept and control algorithm are tested in a number of scenarios representing intersection, lane change and different types of driver's abnormal behaviour. The simulation results show the feasibility and effectiveness of the proposed method.

汽车驾驶员行为模式及其心理因素对可靠性的影响

综述了汽车驾驶员行为的三种典型模式，详细地剖析了汽车人机系统中驾驶员的行为特征，建立了驾驶员行为模式，并推导出计算驾驶员失误率的公式。应用交通心理学的基本观点，根据驾驶员失误的心理指标体系，结合实际交通事故案例分析，阐明了心理因素对驾驶员可靠性的影响及其程度。

汽车驾驶员行为矫正指导量表的研究

从分析驾驶员的个性因素与交通肇事的相关性出发，通过Ｒ型——聚类分析方法，建立了汽车驾驶员行为矫正指导量表，并通过对所建量表信度和效度的检验，为驾驶员深层次教育和安全行为指导提供了一套操作性很强的有效方法和手段。

施工铁路特种车辆司机心理与A行为调查

为了解铁路施工特种车辆司机心理紧张与A行为状况 ,采用SCL - 90、A型行为量表 ,对 5个生产单位的2 31名铁路施工特种车辆司机进行心理学调查。结果显示 ,A、C型行为分别占 10 82 %、8 6 6 %,A- 行为占 2 8 5 7%,B行为占 5 1 95。A型行为SCL - 90的 8个单项因子得分均大于A- 、C、B行为及常模 ,C型行为的有 4个单项因素得分大于全国常模 ,P <0 0 5。说明A型行为人的心理较其他行为人的心理脆弱 ,为此在改善作业场所劳动条件的同时 ,特别要对A、C型行为的人提供心理治疗和健康指导。

山地城市多车道交织区驾驶人心理负荷研究

为明确驾驶人在城市道路多车道交织区路段行驶时的减速行为特性及其与驾驶心理负荷间的相关性,开展实车试验,采集驾驶人心电信号和车辆运行参数等数据,将驾驶人按不同的换道状态分为2种类型,分析驾驶人心率变化特性与减速特征间的关系。研究结果表明:在交织区段内,非干扰状态下驾驶人心理负荷较干扰状态整体明显增大,但后者心理波动更频繁;女性驾驶人驶入交织区段时较男性驾驶人更易产生较大的心理负荷;在干扰与非干扰状态下,驾驶人心率增长率均表现出与最大减速度之间偏相关性较高,即最大减速度对驾驶人心率增长率的影响更大。

道路环境与驾驶行为

驾驶员在道路交通中起着最重要的作用,驾驶行为对交通的安全性影响很大,而驾驶行为与道路环境因素密切相关,本文探讨限速标志和交通警察对驾驶行为的影响,进而提出改进交通管理的措施。

交通污染对公交汽车司售人员神经行为功能的影响

目的 探讨交通污染对司售人员神经行为功能的影响。方法 选择合肥市公交公司 46名男性司机和 2 4名男性售票员为研究对象 ,选择某校在校生 5 2名为对照组 ,采用 WHO神经行为核心测试组合 (WHO- NCTB)对神经行为功能进行调查。结果 司售人员工作环境中的有害因素强度明显高于对照组 ,在行为功能测试中 ,司售人员的平均反应时、目标追踪、数字译码、视觉记忆、数字跨度测试得分与对照组差异有显著性。在情感调查中 ,司机组的疲劳惰性和售票员的紧张焦虑得分与对照组差异有显著性。结论 交通污染严重影响司售人员的身心健康。

Exploring the stimulative effect on following drivers in a consecutive lane change using microscopic vehicle trajectory data

Improper lane-changing behaviours may result in breakdown of traffic flow and the occurrence of various types of collisions.This study investigates lane-changing behaviours of multiple vehicles and the stimulative effect on following drivers in a consecutive lanechanging scenario.The microscopic trajectory data from the HighD dataset are used for driving behaviour analysis.Two discretionary lane-changing vehicle groups constitute a consecutive lane-changing scenario,and not only distance-and speed-related factors but also driving behaviours are taken into account to examine the impacts on the utility of following lane-changing vehicles.A random parameters logit model is developed to capture the driver’s psychological heterogeneity in the consecutive lane-changing situation.Furthermore,a lane-changing utility prediction model is established based on three supervised learning algorithms to detect the improper lane-changing decision.Results indicate that 1)the consecutive lane-changing behaviours have a significant negative effect on the following lane-changing vehicles after lane change;2)the stimulative effect exists in a consecutive lane-change situation and its influence is heterogeneous due to different psychological activities of drivers;and 3)the utility prediction model can be used to detect an improper lane-changing decision.

Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model

We propose a model structure with a double-layer hidden Markov model (HMM) to recognise driving intention and predict driving behaviour. The upper-layer multi-dimensional discrete HMM (MDHMM) in the double-layer HMM represents driving intention in a combined working case, constructed according to the driving behaviours in certain single working cases in the lower-layer multi-dimensional Gaussian HMM (MGHMM). The driving behaviours are recognised by manoeuvring the signals of the driver and vehicle state information, and the recognised results are sent to the upper-layer HMM to recognise driving intentions. Also, driving behaviours in the near future are predicted using the likelihood-maximum method. A real-time driving simulator test on the combined working cases showed that the double-layer HMM can recognise driving intention and predict driving behaviour accurately and efficiently. As a result, the model provides the basis for pre-warning and intervention of danger and improving comfort performance.

Impact of next-nearest leading vehicles on followers’ driving behaviours and traffic stability in mixed traffic

There has been an increase in the number of on-road vehicles of all types,especially in some developing countries.The rise of traffic heterogeneity causes larger mixed traffic conge stion.This study examines the impact of next-nearest leading vehicles on the driving of following drivers in mixed traffic.Although previous studies reported that traffic stability can be improved with the introduction of followers’anticipatory driving that refers to multiple leaders,the effect of anticipatory driving on mixed traffic has not yet been examined.Using data collected from experiments conducted with groups of two and three vehicles,we found that operational delay,maximum acceleration and deceleration of the followers were affected by the presence of next-nearest leaders.In addition,we developed regression models of the affected followers’behaviours with respect to the next-nearest leaders and identified the factors affecting these behaviours.For example,the followers’deceleration is directly affected by the height of the next-nearest leading vehicles.Hence,the model parameters for determining the deceleration of following vehicles should take the height of the next-nearest leading vehicle into consideration.Finally,based on the regression models,we estimated values of parameters in the intelligent driver model when the type of the next-nearest leader was changed.Stability analysis based on these estimated parameters implied that a tall or short next-nearest leader with a large engine power would stabilise traffic when anticipatory driving of followers is possible.

基于“促进疫苗接种的行为和社会驱动因素模型”对我国流感疫苗接种政策的分析

本文采用世界卫生组织于2022年发布的“促进疫苗接种的行为和社会驱动因素”模型,对2022年度我国各级行政区划流感疫苗接种相关政策文件进行了分析。情况显示,我国目前流感疫苗接种政策对“宣传动员”方面已有一定的关注度,但对“接种便利性”重视程度仍不足。建议在继续强化宣传动员的基础上积极探索提升接种便利性的方法,制定相应的提升接种便利性的方案,科学设置接种率目标并鼓励有条件的地区开展重点人群免费接种项目。

基于利益相关者理论视角对我国高校若干支出行为的解释

本文从利益相关者理论的视角出发,尝试建立一个利益均衡模型来描述和解释我国高校支出特征及其影响机制。高校支出行为受到包括高校管理者在内的各类利益相关者的影响,利益相关者在一定的制度环境下形成特定的利益结构,利益结构影响学校的行动选择,从而形成学校支出特征。进一步说,我国高校支出行为的改进应该从调整高校所面临的利益结构开始,使各利益相关者更为关注高校的全面健康可持续发展。

Safety of motorised two wheelers in mixed traffic conditions:Literature review of risk factors

The ownership of motorised two wheelers(MTWs)has been on the rise across various countries across the globe.The growth has been especially higher in developing countries which have typical traffic characteristics and higher populations.This steady rise has resulted in increased accident and fatalities.This abrupt increase warranted attention from the researchers to carry out specific studies for MTWs,which have a very different behaviour as compared to cars in terms of physical and dynamic parameters.Moreover,the unique traffic patterns usually found in the developing countries pose an additional challenge to the researchers,since the conventional focus of transportation safety researchers was a homogeneous car-based traffic.Many such studies have been attempted,especially in the recent decades,which have considered various risk factors related to MTW safety.However,the studies have considered different sets of risk factors and have given surprising and even conflicting results.Therefore,a comprehensive review of the diverse studies needs to be carried out which incorporates all the risk factors considered in previous research.This study reviews such research papers which have analysed various risk factors related to safety of MTWs,especially in heterogeneous,non-lane based traffic.Specifically,this paper aims to incorporate results from those studies and highlight the conclusions from state of the art.The paper also discusses about the research gaps that are crucial for MTW safety in mixed traffic conditions.The review will be useful for researchers working in the field of MTW safety and for policy implementation and analysis.