Accelerated Risk Assessment for Highly Automated Vehicles: Surrogate-Based Monte Carlo Method

To validate whether Highly Automated Vehicles (HAVs) can live up to human expectations, it is essential to estimate their risk rate within the context of the naturalistic driving environment. Due to the low probability of exposure to risky events, the testing process is exceedingly time-consuming. To tackle this issue, we proposed a Surrogate-based Monte Carlo Method to accelerate the risk assessment for HAVs in scenario-based simulation. Surrogate Models (SMs) were utilized to approximate the outcomes of untested scenarios, hence facilitating the identification of risky scenarios. Therefore, the large number of samples required by Monte Carlo did not need to be tested entirely. Naturalistic distributions fitted from HighD data was used to generate samples. The Car-following and Cut-in scenarios were selected for the case study, as they represented two distinct testing spaces. As such, the capabilities of different SMs can be further analyzed. We proved that the performances of six mainstream SMs were greatly distinguished from each other. Inverse Distance Weighted (IDW), the proven most capable SM, was combined with Monte Carlo. Compared with Monte Carlo, up to 95% of tested samples can be saved. Compared with the Importance Sampling method, another popular improvement in Monte Carlo simulation, the proposed method can further save 58.1% of CPU execution time.