Analysis of Unprotected Intersection Left-Turn Conflicts based on Naturalistic Driving Data
For developers of automated vehicle testing environments, this provides a data-driven model for a high-priority pre-crash scenario identified by NHTSA.
This study analyzed nearly 7,000 left-turn/straight-driving conflicts at unprotected intersections using naturalistic driving data, finding that vehicle type significantly influences conflict dynamics while season has limited effect. The resulting stochastic model can simulate LTAP/OD crash scenarios for testing automated vehicles.
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.