Modeling chain collisions in vehicular networks with variable penetration rates

The vehicular ad hoc network has great potential in improving traffic safety. One of the most important and interesting issues in the research community is the safety evaluation with limited penetration rates of vehicles equipped with inter-vehicular communications. In this paper, a stochastic model is proposed for analyzing the vehicle chain collisions. It takes into account the influences of different penetration rates, the stochastic nature of inter-vehicular distance distribution, and the different kinematic parameters related to driver and vehicle. The usability and accuracy of this model is tested and proved by comparative experiments with Monte Carlo simulations. The collision outcomes of a platoon in different penetration rates and traffic scenarios are also analyzed based on this model. These results are useful to provide theoretical insights into the safety control of a heterogeneous platoon.

[1]  Jing Zhou,et al.  Range policy of adaptive cruise control vehicles for improved flow stability and string stability , 2005, IEEE Transactions on Intelligent Transportation Systems.

[2]  Tarik Taleb,et al.  Toward an Effective Risk-Conscious and Collaborative Vehicular Collision Avoidance System , 2010, IEEE Transactions on Vehicular Technology.

[3]  Sheng Chen,et al.  Optimal Beaconing Control for Epidemic Routing in Delay-Tolerant Networks , 2012, IEEE Transactions on Vehicular Technology.

[4]  L. A. Pipes An Operational Analysis of Traffic Dynamics , 1953 .

[5]  Stratis Ioannidis,et al.  Optimal and scalable distribution of content updates over a mobile social network , 2009, IEEE INFOCOM 2009.

[6]  Swaroop Darbha,et al.  Assessing the benefits of coordination in automatically controlled vehicles , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[7]  Mike McDonald,et al.  Towards an understanding of adaptive cruise control , 2001 .

[8]  Woosuk Choi,et al.  Assessing the safety benefits due to coordination amongst vehicles during an emergency braking maneuver , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[9]  Rajesh Rajamani,et al.  Adaptive Cruise Control , 2015, Encyclopedia of Systems and Control.

[10]  D. Shinar,et al.  Effects of uncertainty, transmission type, driver age and gender on brake reaction and movement time. , 2002, Journal of safety research.

[11]  Petros A. Ioannou,et al.  Analysis of traffic flow with mixed manual and semiautomated vehicles , 2003, IEEE Trans. Intell. Transp. Syst..

[12]  Kay Fitzpatrick,et al.  New Stopping Sight Distance Model for Use in Highway Geometric Design , 1998 .

[13]  Minglu Li,et al.  Recognizing Exponential Inter-Contact Time in VANETs , 2010, 2010 Proceedings IEEE INFOCOM.

[14]  Oliver W. W. Yang,et al.  Vehicular telematics over heterogeneous wireless networks: A survey , 2010, Comput. Commun..

[15]  Subir Biswas,et al.  Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety , 2006, IEEE Communications Magazine.

[16]  Steven E. Shladover,et al.  PATH Investigations in Vehicle-Roadside Cooperation and Safety: A Foundation for Safety and Vehicle-Infrastructure Integration Research , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[17]  Andreas Meier,et al.  Design of 5.9 ghz dsrc-based vehicular safety communication , 2006, IEEE Wireless Communications.

[18]  J. Hedrick,et al.  String stability of interconnected systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[19]  Petros A. Ioannou,et al.  Mixed Manual/Semi-Automated Traffic: A Macroscopic Analysis , 2001 .

[20]  Joan Garcia-Haro,et al.  A stochastic model for design and evaluation of chain collision avoidance applications , 2013 .

[21]  N L Schweitzer,et al.  A FIELD STUDY ON BRAKING RESPONSES DURING DRIVING (II). , 1995 .

[22]  John Lygeros,et al.  Longitudinal control of the lead car of a platoon , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[23]  Do Young Eun,et al.  Crossing over the bounded domain: from exponential to power-law intermeeting time in mobile ad hoc networks , 2009, TNET.

[24]  Nicholas F. Maxemchuk,et al.  Highway Capacity Benefits from Using Vehicle-to-Vehicle Communication and Sensors for Collision Avoidance , 2011, 2011 IEEE Vehicular Technology Conference (VTC Fall).

[25]  D. Swaroop,et al.  String Stability Of Interconnected Systems: An Application To Platooning In Automated Highway Systems , 1997 .

[26]  Petros A. Ioannou,et al.  Integrated Roadway/Adaptive Cruise Control System: Safety, Performance, Environmental and Near Term Deployment Considerations , 2004 .

[27]  Hannes Hartenstein,et al.  A tutorial survey on vehicular ad hoc networks , 2008, IEEE Communications Magazine.

[28]  Azim Eskandarian,et al.  Research advances in intelligent collision avoidance and adaptive cruise control , 2003, IEEE Trans. Intell. Transp. Syst..

[29]  Antony Tang,et al.  Collision avoidance timing analysis of DSRC-based vehicles. , 2010, Accident; analysis and prevention.

[30]  E. Montroll,et al.  Traffic Dynamics: Studies in Car Following , 1958 .

[31]  Eric Feron,et al.  Preventing Automotive Pileup Crashes in Mixed-Communication Environments , 2009, IEEE Transactions on Intelligent Transportation Systems.

[32]  Dario G. Liebermann,et al.  A FIELD STUDY ON BRAKING RESPONSES DURING DRIVING. I. TRIGGERING AND MODULATION , 1995 .

[33]  Joan Garcia-Haro,et al.  A Stochastic Model for Chain Collisions of Vehicles Equipped With Vehicular Communications , 2012, IEEE Transactions on Intelligent Transportation Systems.