Virtual safety assessment plays a vital role in evaluating the safety impact of pre-crash safety systems such as advanced driver assistance systems (ADAS) and automated driving systems (ADS). However, as the number of parameters in simulation-based scenario generation increases, the number of crash scenarios to simulate grows exponentially, making complete enumeration computationally infeasible. Efficient sampling methods, such as importance sampling and active sampling, have been proposed to address this challenge. However, a comprehensive evaluation of how domain knowledge, stratification, and batch sampling affect their efficiency remains limited. This study evaluates the performance of importance sampling and active sampling in scenario generation, incorporating two domain-knowledge-driven features: adaptive sample space reduction (ASSR) and stratification. Additionally, we assess the effects of a third feature, batch sampling, on computational efficiency in terms of both CPU and wall-clock time. Based on our findings, we provide practical recommendations for applying ASSR, stratification, and batch sampling to optimize sampling performance. Our results demonstrate that ASSR substantially improves sampling efficiency for both importance sampling and active sampling. When integrated into active sampling, ASSR reduces the root mean squared estimation error (RMSE) of the estimates by up to 90\%. Stratification further improves sampling performance for both methods, regardless of ASSR implementation. When ASSR and/or stratification are applied, importance sampling performs on par with active sampling, whereas when neither feature is used, active sampling is more efficient. Larger batch sizes reduce wall-clock time but increase the number of simulations required to achieve the same estimation accuracy.

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.

The use of virtual safety assessment as the primary method for evaluating vehicle safety technologies has emphasized the importance of crash scenario generation. One of the most common crash types is the rear-end crash, which involves a lead vehicle and a following vehicle. Most studies have focused on the following vehicle, assuming that the lead vehicle maintains a constant acceleration/deceleration before the crash. However, there is no evidence for this premise in the literature. This study aims to address this knowledge gap by thoroughly analyzing and modeling the lead vehicle's behavior as a first step in generating rear-end crash scenarios. Accordingly, the study employed a piecewise linear model to parameterize the speed profiles of lead vehicles, utilizing two rear-end pre-crash/near-crash datasets. These datasets were merged and categorized into multiple sub-datasets; for each one, a multivariate distribution was constructed to represent the corresponding parameters. Subsequently, a synthetic dataset was generated using these distribution models and validated by comparison with the original combined dataset. The results highlight diverse lead-vehicle speed patterns, indicating that a more accurate model, such as the proposed piecewise linear model, is required instead of the conventional constant acceleration/deceleration model. Crashes generated with the proposed models accurately match crash data across the full severity range, surpassing existing lead-vehicle kinematics models in both severity range and accuracy. By providing more realistic speed profiles for the lead vehicle, the model developed in the study contributes to creating realistic rear-end crash scenarios and reconstructing real-life crashes.

This paper provides a general framework for efficiently obtaining the appropriate intervention time for collision avoidance systems to just avoid a rear-end crash. The proposed framework incorporates a driver comfort model and a vehicle model. We show that there is a relationship between driver steering manoeuvres based on acceleration and jerk, and steering angle and steering angle rate profiles. We investigate how four different vehicle models influence the time when steering needs to be initiated to avoid a rear-end collision. The models assessed were: a dynamic bicycle model (DM), a steady-state cornering model (SSCM), a kinematic model (KM) and a point mass model (PMM). We show that all models can be described by a parameter-varying linear system. We provide three algorithms for steering that use a linear system to compute the intervention time efficiently for all four vehicle models. Two of the algorithms use backward reachability simulation and one uses forward simulation. Results show that the SSCM, KM and PMM do not accurately estimate the intervention time for a certain set of vehicle conditions. Due to its fast computation time, DM with a backward reachability algorithm can be used for rapid offline safety benefit assessment, while DM with a forward simulation algorithm is better suited for online real-time usage.