Accelerated Safety Testing for Highly Automated Vehicles: Application and Capability Comparison of Surrogate Models

With the gradual perfection of Highly Automated Vehicles (HAVs), it is obligatory to assess their safety performance in simulation that mirrors the real-world driving environment. However, the minimal likelihood of exposure to risky events can result in an extremely time-consuming testing process. To address this issue, we applied a surrogate-based method to expedite scenario-based simulated safety testing for HAVs. Model-based surrogates can quickly approximate the results of untested scenarios, thereby facilitating the search for risky scenarios. Car-following and Cut-in scenarios were chosen as two representative Operational Design Domains (ODDs) with different dimensions for case study. Thus, the capabilities of various Surrogate Models (SMs) can be examined in depth. Utilizing the HighD data, two testing ODDs were constructed to be consistent with naturalistic distribution. We demonstrated that the performances of six mainstream SMs differ significantly as the frequency of risky scenarios decreases. Additionally, we conducted multiple rounds of tests to compare the stability of SMs. We also presented a proposal on SMs selection according to the complexity of ODDs and the rarity of risky scenarios. Compared with random testing, the surrogate-based method can search for 4 times as many high-risk Car-following scenarios with only 4% of the test resources, showing great potential in accelerating the testing process. Notably, when the targeted scenarios are not rare in high-dimensional ODD, the calculation simplicity of SMs is the most important factor. Even random testing can be a viable option in such circumstances.