  With the rapid advancement of autonomous driving technology, self-driving
cars have become a central focus in the development of future transportation
systems. Scenario generation technology has emerged as a crucial tool for
testing and verifying the safety performance of autonomous driving systems.
Current research in scenario generation primarily focuses on open roads such as
highways, with relatively limited studies on underground parking garages. The
unique structural constraints, insufficient lighting, and high-density
obstacles in underground parking garages impose greater demands on the
perception systems, which are critical to autonomous driving technology.
  This study proposes an accelerated generation method for perception failure
scenarios tailored to the underground parking garage environment, aimed at
testing and improving the safety performance of autonomous vehicle (AV)
perception algorithms in such settings. The method presented in this paper
generates an intelligent testing environment with a high density of perception
failure scenarios by learning the interactions between background vehicles
(BVs) and autonomous vehicles (AVs) within perception failure scenarios.
Furthermore, this method edits the Markov process within the perception failure
scenario data to increase the density of critical information in the training
data, thereby optimizing the learning and generation of perception failure
scenarios. A simulation environment for an underground parking garage was
developed using the Carla and Vissim platforms, with Bevfusion employed as the
perception algorithm for testing. The study demonstrates that this method can
generate an intelligent testing environment with a high density of perception
failure scenarios and enhance the safety performance of perception algorithms
within this experimental setup.
