Evaluation of intersection crashes using naturalistic driving data through the lens of future I-ADAS.

Objective: Intersection advanced driver assistance systems (I-ADAS) with the capability to detect possible collisions and perform evasive braking have the potential to reduce the number of intersection crashes. However, these systems will encounter many challenges caused by the complexity of real-world driving conditions. The purpose of this study is to use real-world naturalistic driving data to conduct an initial exploration of the potential challenges for future I-ADAS in straight crossing path (SCP), left turn across path/lateral direction (LTAP/LD), and left turn across path/opposite direction (LTAP/OD) crash configurations.

Methods: Intersection crashes were selected from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study. The SHRP 2 dataset includes front-facing, driver-facing, rear-facing, and a hands/feet-facing video and vehicle speed, steering, accelerator, and brake time-series data. This data was reviewed to understand driver sightline obstructions, driver distractions, and initiation of driver responses. The estimated time to collision (TTC) from the precipitating event, defined as when either vehicle entered the intersection without the right-of-way, was computed based on the distance to the impact point divided by the current velocity of the subject vehicle.

Results: The median impact speed was 18.0 km/h for SCP and LTAP/LD crashes and 16.1 km/h for LTAP/OD crashes. The median TTC from the precipitating event was 1.35 s for SCP and LTAP/LD crashes and 1.44 s for LTAP/OD crashes. For SCP crashes, the three main sightline obstruction scenarios were slower vehicles traveling in the same direction waiting to turn right, vehicles in the closer crossing lane, and a parked truck. For LTAP/OD crashes, the sightline obstruction was often oncoming vehicles in a closer lane blocking the view of another vehicle.

Conclusion: Sightline obstructions could present a challenge for future I-ADAS to activate in SCP, LTAP/LD, and LTAP/OD crashes. This study utilized naturalistic driving data to complete a comprehensive analysis of intersection crashes, including driver distractions, evasive maneuvers, and sightline obstructions that can assist in the development of I-ADAS. This analysis is not possible with police-reported crash data only, which does not contain necessary details on the driver and surrounding environment.