Determination of functional scenarios for intersection collisions.

INTRODUCTION

The National Highway Traffic Safety Administration (NHTSA) estimated that in 2019, intersection crashes accounted for $179 billion of economic damages and $639 billion in societal damages. Intersection advanced driver assist systems (I-ADASs) and automated driving systems (ADS) are designed and have been actively deployed to avoid or mitigate these intersection crash scenarios. Given the indeterminate parameter space for describing collision scenarios, evaluators, and designers are all challenged with condensing the possible intersection crash configurations into digestible, executable conditions for scenario-based simulation testing. The objective of this study is to identify functional intersection crash configurations for I-ADAS and ADS safety evaluation.

METHODS

Real-world intersection crash characteristics are important considerations for scenario testing as these features can directly correlate to or influence causality, controllability, and potential injury severity. To identify functional intersection crash types, similar crash scenarios were grouped together by identified critical features using an unsupervised decision tree model. A key advantage of this approach was that the implemented cluster crash scenarios would be understandable and interpretable by users. Unsupervised decision trees work by generating uniformly distributed synthetic data with features from real data and classifying all the data as real or synthetic. Long, non-diverging branches were manually pruned to reduce overfitting and improve model performance. Feature importance values were computed based on how effective a given variable grouped the crashes together.

DATA SOURCES

This analysis selected intersection cases that only involved two vehicles from the Crash Investigation Sampling System (CISS) spanning 2017 to 2020. Crash features such as road geometry, intersection signal, and vehicle configuration were important to consider for scenario generation. CISS contained the traffic device, device functionality, vehicle intended pre-event movement, road alignment, road profile, trafficway flow, number of lanes, and crash type for each crash case. Intersection geometry, intersecting road angle, each vehicles' legal moves, and the presence of a two-way-left-turn-lane (TWLTL), channelized roads, bike lanes, crosswalks, street parking, slip lanes, and visual obstructions were manually recorded from the scene diagram.

RESULTS

The tree identified 44 functional intersection crash configurations after pruning. These clusters have five main sections: Straight-crossing path (SCP) crashes at 4-legged intersections, Left-Turn-Across-Path/Opposite Direction (LTAP/OD) crashes at 4-legged intersections, other crash types at 4-legged intersections, roundabout and multileg intersections, and 3-legged intersection crashes. The features that best split the data were TWLTL, lane travel direction violation, and traffic control device functionality. The largest cluster was SCP crashes at 4-legged, undivided intersections where the traffic control device was working and both vehicles did not violate the direction of their lane of travel. This cluster was adjacent to 32 vehicles in similar SCP crashes except a vehicle performed an unexpected maneuver based on their lane position.

CONCLUSION

These 44 identified crash configurations could be useful in bolstering the robustness of I-ADAS and ADS intersection scenario testing as they are a compact representation of all the police reported intersection crashes where a vehicle was towed. Future studies could generate logical scenarios with distributions of initial conditions and behaviors from these clusters that could be used to evaluate an I-ADAS or ADS.