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It is necessary to thoroughly evaluate the effectiveness and safety ofConnected Vehicles (CVs) algorithm before their release and deployment. Currentevaluation approach mainly relies on simulation platform with thesingle-vehicle driving model. The main drawback of it is the lack of networkrealism. To overcome this problem, we extract naturalistic V2V encounters datafrom the database, and then separate the primary vehicle encounter category byclustering. A fast mining algorithm is proposed that can be applied to parallelquery for further process acceleration. 4,500 encounters are mined from a 275GB database collected in the Safety Pilot Model Program in Ann Arbor Michigan,USA. K-means and Dynamic Time Warping (DTW) are used in clustering. Resultsshow this method can quickly mine and cluster primary driving scenarios from alarge database. Our results separate the car-following, intersection andby-passing, which are the primary category of the vehicle encounter. Weanticipate the work in the essay can become a general method to effectivelyextract vehicle encounters from any existing database that contains vehicularGPS information. What's more, the naturalistic data of different vehicleencounters can be applied in Connected Vehicles evaluation.

Extraction of V2V Encountering Scenarios from Naturalistic Driving Database