Extraction of V2V Encountering Scenarios from Naturalistic Driving Database

TL;DR

This paper presents a method to extract and cluster naturalistic vehicle-to-vehicle encounter scenarios from large driving databases, enhancing the realism of connected vehicle evaluations beyond traditional single-vehicle simulations.

Contribution

It introduces a fast mining and clustering algorithm using K-means and DTW to identify primary driving scenarios from large naturalistic driving datasets.

Findings

Successfully mined 4,500 encounters from a 275 GB database

Effectively separated car-following, intersection, and bypassing scenarios

Method accelerates scenario extraction for connected vehicle evaluation

Abstract

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.