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