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.

翻译：随着自动驾驶技术的快速发展，自动驾驶汽车已成为未来交通系统发展的核心焦点。场景生成技术已成为测试和验证自动驾驶系统安全性能的关键工具。当前场景生成的研究主要集中在高速公路等开放道路，针对地下停车库场景的研究相对有限。地下停车库独特的结构约束、照明不足以及高密度障碍物对感知系统提出了更高要求，而感知系统对自动驾驶技术至关重要。本研究提出了一种针对地下停车库环境的感知失效场景加速生成方法，旨在测试和改进自动驾驶汽车感知算法在此类环境中的安全性能。本文提出的方法通过学习感知失效场景中背景车辆与自动驾驶车辆之间的交互，生成具有高密度感知失效场景的智能测试环境。此外，该方法通过编辑感知失效场景数据中的马尔可夫过程，增加训练数据中关键信息的密度，从而优化感知失效场景的学习与生成。研究基于Carla和Vissim平台构建了地下停车库仿真环境，并采用Bevfusion作为测试感知算法。实验表明，该方法能够生成具有高密度感知失效场景的智能测试环境，并在此实验设置中提升感知算法的安全性能。