Model-Based Generation of Representative Rear-End Crash Scenarios Across the Full Severity Range Using Pre-Crash Data

To quantitatively estimate the safety impact of driving automation systems through simulation, it is crucial to use representative baseline pre-crash scenarios. However, such baselines generated through existing methods are generally biased towards either non-severe or severe crashes, as the underlying data used are biased. This study sought to address this issue by combining rear-end pre-crash kinematics data from naturalistic driving and in-depth crash data to create a representative dataset of rear-end crash characteristics across the full severity range in the United States. Multivariate distribution models were built for the combined dataset, and a driver behavior model for the following vehicle was created by combining two existing models. Simulations were conducted to generate a set of synthetic rear-end crash scenarios, which were then weighted to create a representative synthetic rear-end crash dataset. Finally, the synthetic dataset was validated by comparing the distributions of parameters and the outcomes (Delta-v, the total change in vehicle velocity over the duration of the crash event) of the generated crashes with those in the original combined dataset. The synthetic crash dataset can be used for the safety impact assessments of driving automation systems and as a benchmark when evaluating the representativeness of scenarios generated through other methods.