Testing and evaluation are critical steps in the development and deployment of autonomous
vehicles (AVs). This paper aims to provide an online adaptive generation framework for …

Risk assessment is important for intelligent vehicles to make safe driving decisions. In some
researches, the risk is modeled as the effect of each environment element on the ego …

The core value of simulation-based autonomy tests is to create densely extreme traffic
scenarios to test the performance and robustness of the algorithms and systems. Test …

Microscopic traffic simulation provides a controllable, repeatable, and efficient testing
environment for autonomous vehicles (AVs). To evaluate AVs' safety performance …

Traffic modeling is vitally important to the simulation test of autonomous driving, as on-road
testing is not only tedious, inefficient, costly, and often unsafe. The fidelity of the traffic model …

Virtual simulation‐based testing of autonomous vehicles (AVs) needs massive challenging
corner cases to reach high testing accuracy. Current methods achieve this goal by finding …

Scenarios are a crucial element for developing, testing, and verifying autonomous driving
systems. However, open-source scenarios are often formulated using different …

Interaction between the background vehicles (BVs) and automated vehicles (AVs) in
scenario-based testing plays a critical role in evaluating the intelligence of the AVs. Current …

Autonomous driving algorithms are always in operation during normal driving and require
responses to many more driving situations than ADAS, which assists the driver only in …

As driving scenarios and autonomous vehicles (AVs) become increasingly intricating, there
is an increasing need for innovative frameworks that can enhance and test AV capabilities …