Review on car-following sensor based and data-generation mapping for safety and traffic management and road map toward ITS

Abstract As the number of technologies implemented in our daily life and rapidly deployed in the transportation system continuously increases, the car-following model is crucial in the transition towards developing an intelligent transportation system. The car-following model adopts different types of sensors to collect driver behaviour for developing road safety and efficient traffic management. This systematic literature review aims to examine previous research to highlight several insights into a number of issues, problems and challenges encountered in research over the last 11 years. Three major databases (i.e. IEEE Xplore®, ScienceDirect and Web of Science) are scanned and surveyed to locate highly cited peer-reviewed studies. Amongst the 714 articles found, only 126 are analysed to map the car-following model research area. Two phases of articles filtration process are implemented on collected articles. The first phase is performed using inclusion criteria like development articles written in English language and development/review articles discussed the integration of car-following model applications in the process of driver behaviour characterisation. Then, the resulted articles from the first phase are included in the second phase for further filtration. The second phase of filtration is achieved using more specific inclusion criteria, i.e. the articles that discuss data acquisition system (DAS) and the integration of car-following model in driver behaviour characterisation. The final set of articles is categorised in three main categories: review articles (5/126), learning-based development articles (16/126) and non-learning-based development articles (105/126). The taxonomy of grouping development studies is created in accordance with the type of dataset used for development. A number of motivational topics have been reported to pursue research development in this area. Recommendations for different stakeholders regarding several valuable points are provided to facilitate and accelerate development in the car-following context. Substantial analysis is performed to identify research gaps in the experimental methodologies applied in the literature. This analysis is conducted on the basis of the methodological aspects of data collection to identify the weaknesses of the current literature. A research map is drawn to provide several insights into potential key points towards the advancement of this research area.

[1]  Giuseppe Guido,et al.  Safety performance measures: a comparison between microsimulation and observational data , 2011 .

[2]  Sajad Shiravi,et al.  A video-based approach to calibrating car-following parameters in VISSIM for urban traffic , 2016 .

[3]  Hai-Jun Huang,et al.  A car-following model with real-time road conditions and numerical tests , 2014 .

[4]  C Michael Walton,et al.  Cross-validating traffic speed measurements from probe and stationary sensors through state reconstruction , 2019 .

[5]  Shu Ma,et al.  Type 2 diabetes can undermine driving performance of middle-aged male drivers through its deterioration of perceptual and cognitive functions. , 2019, Accident; analysis and prevention.

[6]  Zuduo Zheng,et al.  Incorporating human-factors in car-following models : a review of recent developments and research needs , 2014 .

[7]  Oscar Oviedo-Trespalacios,et al.  The impact of road advertising signs on driver behaviour and implications for road safety: A critical systematic review , 2019, Transportation Research Part A: Policy and Practice.

[8]  Mark Vollrath,et al.  Situational influences on response time and maneuver choice: Development of time-critical scenarios. , 2019, Accident; analysis and prevention.

[9]  Xiqun Chen,et al.  Asymmetric stochastic Tau Theory in car-following , 2013 .

[10]  Ludovic Leclercq,et al.  Do microscopic merging models reproduce the observed priority sharing ratio in congestion , 2009 .

[11]  Loo Hay Lee,et al.  Enhancing transportation systems via deep learning: A survey , 2019, Transportation Research Part C: Emerging Technologies.

[12]  Ashley Martin,et al.  Speed choice and driving performance in simulated foggy conditions. , 2011, Accident; analysis and prevention.

[13]  A. A. Zaidan,et al.  Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions , 2018 .

[14]  Alexandra Kondyli,et al.  Macroscopic and microscopic analyses of managed lanes on freeway facilities in South Florida , 2017 .

[15]  Dirk Helbing,et al.  Adaptive cruise control design for active congestion avoidance , 2008 .

[16]  Chunming Qiao,et al.  The effects of warning characteristics on driver behavior in connected vehicles systems with missed warnings. , 2019, Accident; analysis and prevention.

[17]  Soon Ae Chun,et al.  Traffic evacuation simulation based on multi-level driving decision model , 2017 .

[18]  Jian Zhang,et al.  Virtual traffic simulation with neural network learned mobility model , 2018, Adv. Eng. Softw..

[19]  Martin Treiber,et al.  Calibrating Car-Following Models by Using Trajectory Data , 2008, 0803.4063.

[20]  Changxu Wu,et al.  A fuel economy optimization system with applications in vehicles with human drivers and autonomous vehicles , 2011 .

[21]  Qiang Zhang,et al.  Driving decision-making analysis of car-following for autonomous vehicle under complex urban environment , 2016, 2016 9th International Symposium on Computational Intelligence and Design (ISCID).

[22]  Futong Qin,et al.  Design and Implementation of Vision-Based Intelligent Micro-vehicles Interaction System , 2013, 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[23]  Yiheng Feng,et al.  Estimating Freeway Travel Times Using the General Motors Model , 2016 .

[24]  Xiaohui Zhang,et al.  Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment , 2018, Transportation Research Part C: Emerging Technologies.

[25]  Fuad A. Ghaleb.  Context-Aware Misbehavior Detection Scheme for Vehicular Ad Hoc Networks using Sequential Analysis of the Temporal and Spatial Correlation of the Cooperative Awareness Messages , 2019, Veh. Commun..

[26]  Wei Wang,et al.  Development of a variable speed limit strategy to reduce secondary collision risks during inclement weathers. , 2014, Accident; analysis and prevention.

[27]  Mike McDonald,et al.  Determinants of following headway in congested traffic , 2009 .

[28]  Wu Xiaorui,et al.  A Lane Change Model with the Consideration of Car Following Behavior , 2013 .

[29]  Chao Deng,et al.  Modeling the effect of limited sight distance through fog on car-following performance using QN-ACTR cognitive architecture , 2018, Transportation Research Part F: Traffic Psychology and Behaviour.

[30]  René van Paassen,et al.  Haptic gas pedal support during visually distracted car following , 2010, IFAC HMS.

[31]  B. van Arem,et al.  A generic multi-level framework for microscopic traffic simulation with automated vehicles in mixed traffic , 2020, Transportation Research Part C: Emerging Technologies.

[32]  Hirofumi Aoki,et al.  A Study on the Method for Predicting the Driver's Car-Following Tendency , 2013 .

[33]  Reza Sabzevari,et al.  Multisensor Data Fusion Strategies for Advanced Driver Assistance Systems , 2009 .

[34]  Mehrdad Sabetzadeh,et al.  An extended systematic literature review on provision of evidence for safety certification , 2014, Inf. Softw. Technol..

[35]  Constantinos Antoniou,et al.  Towards data-driven car-following models , 2015 .

[36]  Moshe Ben-Akiva,et al.  Dealing with uncertainty in detailed calibration of traffic simulation models for safety assessment , 2015 .

[37]  Roberta Di Pace,et al.  Development and testing of a fully Adaptive Cruise Control system , 2013 .

[38]  Álvaro Seco,et al.  Calibration of the Gipps Car-following Model Using Trajectory Data , 2014 .

[39]  Xiaogang Jin,et al.  Video-based personalized traffic learning , 2013, Graph. Model..

[40]  Hao Xu,et al.  Influences of Leading-Vehicle Types and Environmental Conditions on Car-Following Behavior , 2016 .

[41]  Brian Henson,et al.  The influence of driver’s mood on car following and glance behaviour: Using cognitive load as an intervention , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

[42]  Shinji Tanaka,et al.  Evaluation of Vehicle Control Algorithm to Avoid Conflicts in Weaving Sections under Fully-controlled Condition in Urban Expressway , 2017 .

[43]  Weiming Liu,et al.  The fault-tolerant control strategy of the Takagi-Sugeno fuzzy car following model with two-delays , 2016, 2016 International Conference on Advanced Robotics and Mechatronics (ICARM).

[44]  Dominique Gruyer,et al.  A computational model of the car driver interfaced with a simulation platform for future Virtual Human Centred Design applications: COSMO-SIVIC , 2012, Eng. Appl. Artif. Intell..

[45]  Tomer Toledo,et al.  A stochastic car following model , 2016 .

[46]  Vinny Cahill,et al.  Real-Time Estimation of Drivers' Behaviour , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[47]  Xuesong Zhou,et al.  Method for investigating intradriver heterogeneity using vehicle trajectory data: A Dynamic Time Warping approach , 2015 .

[48]  Zhi Jun Long,et al.  An Adaptive Network Fuzzy Inference System Controller for Car Following Behavior , 2011 .

[49]  Myoungho Sunwoo,et al.  Ego-Vehicle Speed Prediction Using a Long Short-Term Memory Based Recurrent Neural Network , 2019, International Journal of Automotive Technology.

[50]  S Yousif,et al.  Close following behavior: Testing visual angle car following models using various sets of data , 2011 .

[51]  Xiaogang Jin,et al.  Detailed traffic animation for urban road networks , 2012, Graph. Model..

[52]  Riender Happee,et al.  The effects of time pressure on driver performance and physiological activity: a driving simulator study , 2016 .

[53]  Rui Fu,et al.  Human-like car-following model for autonomous vehicles considering the cut-in behavior of other vehicles in mixed traffic. , 2019, Accident; analysis and prevention.

[54]  Peter Wagner,et al.  Analyzing fluctuations in car-following , 2012 .

[55]  Giovanni Maria Farinella,et al.  On-board monitoring system for road traffic safety analysis , 2018, Comput. Ind..

[56]  Meixin Zhu,et al.  Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study , 2018, Transportation Research Part C: Emerging Technologies.

[57]  Marco Dozza,et al.  Driving context influences drivers' decision to engage in visual-manual phone tasks: Evidence from a naturalistic driving study. , 2015, Journal of safety research.

[58]  Xiqun Chen,et al.  Bayesian network for red-light-running prediction at signalized intersections , 2018, J. Intell. Transp. Syst..

[59]  Keqiang Li,et al.  Driving safety field theory modeling and its application in pre-collision warning system , 2016 .

[60]  Weining Liu,et al.  Linear stability and nonlinear analyses of traffic waves for the general nonlinear car-following model with multi-time delays , 2018, Physica A: Statistical Mechanics and its Applications.

[61]  Ashish Bhaskar,et al.  A pattern recognition algorithm for assessing trajectory completeness , 2018, Transportation Research Part C: Emerging Technologies.

[62]  Pravin Varaiya,et al.  Hardware-In-The-Loop On-ramp Simulation Tool to Debug and Test the Universal Ramp Metering Software , 2009, CTS 2009.

[63]  Volker Hargutt,et al.  Frequency and impact of hands-free telephoning while driving - results from naturalistic driving data , 2015 .

[64]  Akhilesh Kumar Maurya,et al.  Characteristics of lateral vehicular interactions in heterogeneous traffic with weak lane discipline , 2017 .

[65]  Jie Sun,et al.  Length-based vehicle classification using event-based loop detector data , 2014 .

[66]  Jing Shi,et al.  Analysis of impact of elderly drivers on traffic safety using ANN based car-following model , 2020 .

[67]  Sicco Verwer,et al.  Car-following Behavior Model Learning Using Timed Automata , 2017 .

[68]  Tsung Hua Hsu,et al.  Implementation of car-following system using LiDAR detection , 2012, 2012 12th International Conference on ITS Telecommunications.

[69]  B. B. Zaidan,et al.  Smart Home-based IoT for Real-time and Secure Remote Health Monitoring of Triage and Priority System using Body Sensors: Multi-driven Systematic Review , 2019, Journal of Medical Systems.

[70]  Klaus C. J. Dietmayer,et al.  Driver intention inference with vehicle onboard sensors , 2009, 2009 IEEE International Conference on Vehicular Electronics and Safety (ICVES).

[71]  Alexander Verbraeck,et al.  A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters , 2018, Transportation Research Part C: Emerging Technologies.

[72]  Xiao Qi,et al.  Simultaneous modeling of car-following and lane-changing behaviors using deep learning , 2019, Transportation Research Part C: Emerging Technologies.

[73]  Mohammad Jalayer,et al.  A hybrid approach for identifying factors affecting driver reaction time using naturalistic driving data , 2019, Transportation Research Part C: Emerging Technologies.

[74]  Tsung Hua Hsu,et al.  A path planning achievement of car following in motion control via LiDAR sensing , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[75]  Michael Schrauf,et al.  EEG alpha spindles and prolonged brake reaction times during auditory distraction in an on-road driving study. , 2014, Accident; analysis and prevention.

[76]  Motoyuki Akamatsu,et al.  Modeling and prediction of driver preparations for making a right turn based on vehicle velocity and traffic conditions while approaching an intersection , 2008 .

[77]  Li Li,et al.  Vehicle headway modeling and its inferences in macroscopic/microscopic traffic flow theory: A survey , 2017 .

[78]  Juneyoung Park,et al.  Developing an algorithm to assess the rear-end collision risk under fog conditions using real-time data , 2018 .

[79]  Mahmood Fathy,et al.  Driver behavior detection and classification using deep convolutional neural networks , 2020, Expert Syst. Appl..

[80]  Li Gao,et al.  A Collision Warning Device Based on the Emergency Braking Behavior Prediction , 2017 .

[81]  Alejandra Medina Flintsch,et al.  A rule-based neural network approach to model driver naturalistic behavior in traffic , 2013 .

[82]  Majid Sarvi,et al.  Simulation of safety: a review of the state of the art in road safety simulation modelling. , 2014, Accident; analysis and prevention.

[83]  Haitham Al-Deek,et al.  Modeling Driver Behavior in Work and Nonwork Zones , 2015 .

[84]  Zhonghai Li,et al.  An empirical investigation of a dynamic brake light concept for reduction of rear-end collisions through manipulation of optical looming , 2008, Int. J. Hum. Comput. Stud..

[85]  Xiaoliang Ma,et al.  A model identification scheme for driver-following dynamics in road traffic , 2013 .

[86]  Stephane Hess,et al.  Combining driving simulator and physiological sensor data in a latent variable model to incorporate the effect of stress in car-following behaviour , 2019, Analytic Methods in Accident Research.

[87]  Ramachandran Balakrishna,et al.  Large-Scale Traffic Simulation Tools for Planning and Operations Management , 2009, CTS 2009.

[88]  Stephane Hess,et al.  Modelling the effects of stress on gap-acceptance decisions combining data from driving simulator and physiological sensors , 2018, Transportation Research Part F: Traffic Psychology and Behaviour.

[89]  S. Soccolich,et al.  Driver visual behavior while using adaptive cruise control on commercial motor vehicles , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

[90]  Xiaomeng Li,et al.  A rear-end collision risk assessment model based on drivers' collision avoidance process under influences of cell phone use and gender-A driving simulator based study. , 2016, Accident; analysis and prevention.

[91]  Marjan Kuchaki Rafsanjani,et al.  QMM-VANET: An efficient clustering algorithm based on QoS and monitoring of malicious vehicles in vehicular ad hoc networks , 2020, J. Syst. Softw..

[92]  Dimitar Petrov Filev,et al.  Real-time Determination of Driver's Driving Behavior during Car Following , 2015 .

[93]  Vincenzo Punzo,et al.  Efficient calibration of microscopic car-following models for large-scale stochastic network simulators , 2019, Transportation Research Part B: Methodological.

[94]  Thiagalingam Kirubarajan,et al.  Multi-Vehicle Tracking With Road Maps and Car-Following Models , 2018, IEEE Transactions on Intelligent Transportation Systems.

[95]  Long Chen,et al.  Dynamic Neural Network-Based Integrated Learning Algorithm for Driver Behavior , 2012 .

[96]  Mark Vollrath,et al.  Expectations while car following--the consequences for driving behaviour in a simulated driving task. , 2010, Accident; analysis and prevention.

[97]  Keith Redmill,et al.  Collecting ambient vehicle trajectories from an instrumented probe vehicle: High quality data for microscopic traffic flow studies , 2016 .

[98]  M. Byrnes,et al.  Posttraumatic growth after burn in adults: An integrative literature review. , 2017, Burns : journal of the International Society for Burn Injuries.

[99]  Samuel G Charlton,et al.  The role of looming and attention capture in drivers' braking responses. , 2008, Accident; analysis and prevention.

[100]  Shaobing Xu,et al.  A geometry-driven car-following distance estimation algorithm robust to road slopes , 2019, Transportation Research Part C: Emerging Technologies.

[101]  Eugenio Morello,et al.  Impact Analysis of Ecodriving Behaviour Using Suitable Simulation Platform (ICT-EMISSIONS Project) , 2016 .

[102]  Giuseppe Guido,et al.  Effects of calibration process on the simulation of rear-end conflicts at roundabouts , 2019, Journal of Traffic and Transportation Engineering (English Edition).

[103]  Soyoung Ahn,et al.  Stochastic Modeling of Breakdown at Freeway Merge Bottleneck , 2017 .

[104]  Miss Laiha Mat Kiah,et al.  Comprehensive review and analysis of anti-malware apps for smartphones , 2019, Telecommunication Systems.

[105]  Nazmul Haque,et al.  VISCAL: Heuristic Algorithm Based Application Tool to Calibrate Microscopic Simulation Parameters , 2017 .

[106]  Sebastien Glaser,et al.  Multi-Criteria Decision Making for Autonomous Vehicles using Fuzzy Dempster-Shafer Reasoning , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[107]  Pongsathorn Raksincharoensak,et al.  Estimation of Driving Performance Level Using Longitudinal and Lateral Driver Models , 2013 .

[108]  Hironori Suzuki,et al.  Dynamic Estimation of Headway Distance in Vehicle Platoon System under Unexpected Car-following Situations , 2015 .

[109]  Bin Jia,et al.  Improved 2D intelligent driver model in the framework of three-phase traffic theory simulating synchronized flow and concave growth pattern of traffic oscillations , 2016 .

[110]  Martin Treiber,et al.  Microscopic Calibration and Validation of Car-Following Models – A Systematic Approach , 2013, 1403.4990.

[111]  Yulan Liang,et al.  Driver performance effects of simultaneous visual and cognitive distraction and adaptation behavior , 2012 .

[112]  Rui Chai,et al.  The Effects of Stop-and-go Wave on the Immediate Follower and Change in Driver Characteristics , 2016 .

[113]  Meixin Zhu,et al.  Impact on car following behavior of a forward collision warning system with headway monitoring , 2020 .

[114]  Neville A. Stanton,et al.  Adaptive driver modelling in ADAS to improve user acceptance: A study using naturalistic data , 2019, Safety Science.

[115]  Gennaro Nicola Bifulco,et al.  Data collection for traffic and drivers' behaviour studies: a large-scale survey , 2014 .

[116]  P. Lardelli-Claret,et al.  Efficacy of training with driving simulators in improving safety in young novice or learner drivers: A systematic review , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

[117]  Valentina E. Balas,et al.  Monoscopic CCD Cameras as Distance Sensors , 2009 .

[118]  Loukas Dimitriou,et al.  Assessing rear-end crash potential in urban locations based on vehicle-by-vehicle interactions, geometric characteristics and operational conditions. , 2018, Accident; analysis and prevention.

[119]  Yang Li,et al.  Modeling Longitudinal Following Control Based on Preceding Vehicle Motion Predictor* , 2019, 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

[120]  Mike McDonald,et al.  Driving behaviour models enabling the simulation of Advanced Driving Assistance Systems: revisiting the Action Point paradigm , 2013 .

[121]  Colin Christopher Caprani,et al.  Traffic load patterning on long span bridges: A rational approach , 2019 .

[122]  Wuhong Wang,et al.  A safety-based approaching behavioural model with various driving characteristics , 2011 .

[123]  Lidia Montero,et al.  Notes on using simulation-optimization techniques in traffic simulation , 2017 .

[124]  Xuesong Zhou,et al.  Estimating risk effects of driving distraction: a dynamic errorable car-following model , 2015 .

[125]  Mahdi Karbasian,et al.  The application of ISM model in evaluating agile suppliers selection criteria and ranking suppliers using fuzzy TOPSIS-AHP methods , 2015, Expert Syst. Appl..

[126]  Leonidas Ntziachristos,et al.  Improving fuel consumption and CO2 emissions calculations in urban areas by coupling a dynamic micro traffic model with an instantaneous emissions model , 2017, Transportation Research Part D: Transport and Environment.

[127]  Alfonso Montella,et al.  Longitudinal control behaviour: Analysis and modelling based on experimental surveys in Italy and the UK. , 2016, Accident; analysis and prevention.

[128]  Óscar Mata-Carballeira,et al.  An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance , 2019, Sensors.

[129]  Stratis Kanarachos,et al.  Smartphones as an integrated platform for monitoring driver behaviour: The role of sensor fusion and connectivity , 2018, Transportation Research Part C: Emerging Technologies.

[130]  Baocai Yin,et al.  Real-virtual consistent traffic flow interaction , 2019, Graph. Model..

[131]  Gennaro Nicola Bifulco,et al.  Experimental evidence supporting simpler Action Point paradigms for car-following , 2015 .

[132]  Reza Vatani Nezafat,et al.  Offline reconstruction of missing vehicle trajectory data from 3D LIDAR , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[133]  Chaozhong Wu,et al.  The effect of fatigue driving on car following behavior , 2016 .

[134]  Mohammed Quddus,et al.  A new integrated collision risk assessment methodology for autonomous vehicles. , 2019, Accident; analysis and prevention.

[135]  Mao-Bin Hu,et al.  On some experimental features of car-following behavior and how to model them , 2015 .

[136]  Markos Papageorgiou,et al.  Model predictive control for multi-lane motorways in presence of VACS , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[137]  Ben-Jye Chang,et al.  Cloud Computing-Based Analyses to Predict Vehicle Driving Shockwave for Active Safe Driving in Intelligent Transportation System , 2020, IEEE Transactions on Intelligent Transportation Systems.

[138]  Monica N. Lees,et al.  Risky car following in abstinent users of MDMA. , 2010, Accident; analysis and prevention.

[139]  Gopal Krishna Kamath,et al.  String and robust stability of connected vehicle systems with delayed feedback , 2018 .

[140]  Yang Liu,et al.  Effects of foggy conditions on drivers’ speed control behaviors at different risk levels , 2014 .

[141]  Limin Jia,et al.  Queue length estimation at signalized intersections based on magnetic sensors by different layout strategies , 2017 .

[142]  John L. Zhou,et al.  Eco-driving technology for sustainable road transport: A review , 2018, Renewable and Sustainable Energy Reviews.

[143]  Nicole van Nes,et al.  The potential of naturalistic driving for in-depth understanding of driver behavior: UDRIVE results and beyond , 2019, Safety Science.