Prediction of instantaneous driving safety in emergency scenarios based on connected vehicle basic safety messages

Basic safety message (BSM) is a core subset of standard protocols for connected vehicle system to transmit related safety information via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Although some safety prototypes of connected vehicle have been proposed with effective strategies, few of them are fully evaluated in terms of the significance of BSM messages on performance of safety applications when in emergency.,To address this problem, a data fusion method is proposed to capture the vehicle crash risk by extracting critical information from raw BSMs data, such as driver volition, vehicle speed, hard accelerations and braking. Thereafter, a classification model based on information-entropy and variable precision rough set (VPRS) is used for assessing the instantaneous driving safety by fusing the BSMs data from field test, and predicting the vehicle crash risk level with the driver emergency maneuvers in the next short term.,The findings and implications are discussed for developing an improved warning and driving assistant system by using BSMs messages.,The findings of this study are relevant to incorporation of alerts, warnings and control assists in V2V applications of connected vehicles. Such applications can help drivers identify situations where surrounding drivers are volatile, and they may avoid dangers by taking defensive actions.

[1]  Eungcheol Kim,et al.  Estimates of Critical Values of Aggressive Acceleration from a Viewpoint of Fuel Consumption and Emissions , 2013 .

[2]  Klaus C. J. Dietmayer,et al.  Situation Assessment of an Autonomous Emergency Brake for Arbitrary Vehicle-to-Vehicle Collision Scenarios , 2009, IEEE Transactions on Intelligent Transportation Systems.

[3]  Tieniu Tan,et al.  Traffic accident prediction using 3-D model-based vehicle tracking , 2004, IEEE Transactions on Vehicular Technology.

[4]  Mato Baotic,et al.  Multi-object Adaptive Cruise Control , 2008, HSCC.

[5]  Jinxian Weng,et al.  Analysis of work zone rear-end crash risk for different vehicle-following patterns. , 2014, Accident; analysis and prevention.

[6]  Gerd Wanielik,et al.  Situation Assessment for Automatic Lane-Change Maneuvers , 2010, IEEE Transactions on Intelligent Transportation Systems.

[7]  Lennart Svensson,et al.  A New Vehicle Motion Model for Improved Predictions and Situation Assessment , 2011, IEEE Transactions on Intelligent Transportation Systems.

[8]  P. Albert,et al.  Do elevated gravitational-force events while driving predict crashes and near crashes? , 2012, American journal of epidemiology.

[9]  Geoffrey D. Sullivan,et al.  Filter for Car Tracking Based on Acceleration and Steering Angle , 1996, BMVC.

[10]  Wei Wang,et al.  Calibration of crash risk models on freeways with limited real-time traffic data using Bayesian meta-analysis and Bayesian inference approach. , 2015, Accident; analysis and prevention.

[11]  J. G. Kretzschmar,et al.  Environmental effects of driving behaviour and congestion related to passenger cars , 2000 .

[12]  Sebastian Thrun,et al.  Junior: The Stanford entry in the Urban Challenge , 2008, J. Field Robotics.

[13]  X. R. Li,et al.  Survey of maneuvering target tracking. Part I. Dynamic models , 2003 .

[14]  Asad J. Khattak,et al.  Delivering improved alerts, warnings, and control assistance using basic safety messages transmitted between connected vehicles ☆ , 2016 .

[15]  Y. Kishimoto,et al.  A modeling method for predicting driving behavior concerning with driver’s past movements , 2008, 2008 IEEE International Conference on Vehicular Electronics and Safety.

[16]  Sebastian Thrun,et al.  Model based vehicle detection and tracking for autonomous urban driving , 2009, Auton. Robots.

[17]  Guoyuan Wu,et al.  Evaluating the effectiveness of V2V-based Lane Speed Monitoring application: A simulation study , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[18]  Huei Peng,et al.  Methodology for assessing adaptive cruise control behavior , 2003, IEEE Trans. Intell. Transp. Syst..

[19]  Thomas A. Dingus,et al.  The 100-Car Naturalistic Driving Study Phase II – Results of the 100-Car Field Experiment , 2006 .

[20]  Imre Horváth,et al.  Progress with situation assessment and risk prediction in advanced driver assistance systems: A survey , 2009 .

[21]  Cheol Mun,et al.  Demo: Development of an integrated Cooperative Collision Warning system based on established standards , 2015, 2015 IEEE Vehicular Networking Conference (VNC).

[22]  Jianqiang Wang,et al.  Driving risk assessment using near-crash database through data mining of tree-based model. , 2015, Accident; analysis and prevention.

[23]  J. Gunnarsson,et al.  Joint Driver Intention Classification and Tracking of Vehicles , 2006, 2006 IEEE Nonlinear Statistical Signal Processing Workshop.

[24]  Xiugang Li,et al.  Predicting motor vehicle crashes using Support Vector Machine models. , 2008, Accident; analysis and prevention.

[25]  Dirk Haehnel,et al.  Junior: The Stanford entry in the Urban Challenge , 2008 .

[26]  Aia Sadek,et al.  Safety improvement in vehiclar communication systems , 2017, 2017 12th International Conference on Computer Engineering and Systems (ICCES).

[27]  Luigi Fortuna,et al.  Reactive navigation through multiscroll systems: from theory to real-time implementation , 2008, Auton. Robots.

[28]  In-Kyoo Park,et al.  A variable-precision information-entropy rough set approach for job searching , 2015, Inf. Syst..

[29]  Osama A. Osman,et al.  Impact of Time-to-Collision Information on Driving Behavior in Connected Vehicle Environments Using A Driving Simulator Test Bed , 2015 .