Analysis of Unprotected Intersection Left-Turn Conflicts based on Naturalistic Driving Data

TL;DR

This study analyzes nearly 7,000 naturalistic driving events involving left-turn conflicts at unprotected intersections to develop a stochastic model, aiding automated vehicle testing and safety evaluation.

Contribution

It introduces a comprehensive stochastic model of LTAP/OD conflicts based on real-world data, highlighting vehicle type as a key factor and supporting improved HAV testing environments.

Findings

Vehicle type significantly affects conflict characteristics.

Seasonal changes have limited impact on conflict statistics.

Model can simulate top-priority pre-crash scenarios for vehicle safety testing.

Abstract

Analyzing and reconstructing driving scenarios is crucial for testing and evaluating automated vehicles. This research analyzed left turn / straight-driving conflicts at unprotected intersections by extracting actual vehicle motion data from a naturalistic driving database collected by the University of Michigan. Nearly 7,000 Left turn across path opposite direction (LTAP/OD) events involving heavy trucks and light vehicles were extracted and used to build a stochastic model of such LTAP/OD scenarios. Statistical analysis showed that vehicle type is a significant factor, whereas the change of season seems to have limited influence on the statistical nature of the conflict. The results can be used to build HAV testing environments to simulate the LTAP/OD crash cases in a stochastic manner, which is among the top NHTSA identified priority light-vehicle pre-crash scenarios.