Model-based generation of representative rear-end crash scenarios across the full severity range using pre-crash data

Abstract

Generating representative rear-end crash scenarios is crucial for safety assessments of Advanced Driver Assistance Systems (ADAS) and Automated Driving systems (ADS). However, existing methods for scenario generation face challenges such as limited and biased in-depth crash data and difficulties in validation. This study sought to overcome these challenges by combining naturalistic driving data and pre-crash kinematics data from rear-end crashes. The combined dataset was weighted 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 assessments of ADAS and ADS and as a benchmark when evaluating the representativeness of scenarios generated through other methods.

Figures (15)

• Figure 1: Three selected segments of the lead-vehicle speed profile in a rear-end crash.
• Figure 2: Flowchart of the methodology. Step 0 (the lead-vehicle kinematics model) was performed in our previous study wu2024modeling.
• Figure 3: Flowchart of the data combination. Reference and raw datasets are marked by solid and dashed lines, respectively. Colored arrows and numbers indicate sub-steps. The outcome is the dataset REF_b, the reference dataset of $d_{init}$, $v_{f,init}$, $a_{f,min}$, $v_{l,init}$, and $a_{l,min}$.
• Figure 4: Scatter plot of $v_{f,init}$ and $v_{l,init}$ for COM_b. In most cases, $v_{l,init}$ is no larger than $v_{f,init}$.
• Figure 5: Examples of speed profile fit results. $n_b$ is the number of breakpoints.