Utilization Of Broadcast Methods For Detection Of The Road Conditions In VANET

Vehicle to vehicle communication (V2VC) is one of the modern approaches for exchanging and generating traffic information with (yet to be realised) potential to improve road safety, driving comfort and traffic control. In this paper, we present a novel algorithm which is based on V2V communication, uses in-vehicle sensor information and in collaboration with the other vehicles’ sensor information can detect road conditions and determine the geographical area where this road condition exists – e.g. geographical area where there is traffic density, unusual traffic behaviour, a range of weather conditions (raining), etc. The built-in automatic geographical restriction of the data collection, aggregation and dissemination mechanisms allows warning messages to be received by other cars, not necessarily sharing the identified road condition, which may then be used to identify the optimum route taken by the vehicle e.g. avoid bottlenecks or dangerous areas including accidents or congestions on their current routes. The Traffic Condition Detection Algorithm (TCDA) which we propose here is simple, flexible and fast and does not rely on any kind of roadside infrastructure equipment. It will offer live road conditions information channels at almost no cost to the drivers and public/private traffic agencies and has the potential to become indispensable part of any future intelligent traffic system (ITS). The benefits from applying this algorithm in traffic networks are identified and quantified through building a simulation model for the widely used Network Simulator II (NS2).

[1]  Andrzej Bargiela,et al.  Probabilistic Topic Models for Learning Terminological Ontologies , 2010, IEEE Transactions on Knowledge and Data Engineering.

[2]  Evtim Peytchev,et al.  Auto-sensing and distribution of traffic information in vehicular ad hoc networks , 2004 .

[3]  Andrzej Bargiela,et al.  Human-Centric Information Processing Through Granular Modelling , 2009, Human-Centric Information Processing Through Granular Modelling.

[4]  Deborah Estrin,et al.  The impact of data aggregation in wireless sensor networks , 2002, Proceedings 22nd International Conference on Distributed Computing Systems Workshops.

[5]  Kenneth P. Laberteaux,et al.  Efficient coordination and transmission of data for cooperative vehicular safety applications , 2006, VANET '06.

[6]  Maxim Raya,et al.  The security of vehicular ad hoc networks , 2005, SASN '05.

[7]  Andrzej Bargiela,et al.  Fuzzy clustering with semantically distinct families of variables: Descriptive and predictive aspects , 2010, Pattern Recognit. Lett..

[8]  Andrzej Bargiela,et al.  Incremental Update of Fuzzy Rule-Based Classifiers for Dynamic Problems , 2012 .

[9]  Evtim Peytchev,et al.  Mathematical justification of a heuristic for statistical correlation of real-life time series , 2009, Eur. J. Oper. Res..

[10]  Evtim Peytchev,et al.  Intelligent Transportation Systems-towards integrated framework for traffic/transport telematics applications , 2001, IEEE 54th Vehicular Technology Conference. VTC Fall 2001. Proceedings (Cat. No.01CH37211).

[11]  Andrzej Bargiela,et al.  An Optimization of Allocation of Information Granularity in the Interpretation of Data Structures: Toward Granular Fuzzy Clustering , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).