Against the backdrop of advancing science and technology, autonomous vehicle
technology has emerged as a focal point of intense scrutiny within the academic
community. Nevertheless, the challenge persists in guaranteeing the safety and
reliability of this technology when navigating intricate scenarios. While a
substantial portion of autonomous driving research is dedicated to testing in
open-air environments, such as urban roads and highways, where the myriad
variables at play are meticulously examined, enclosed indoor spaces like
underground parking lots have, to a significant extent, been overlooked in the
scholarly discourse. This discrepancy highlights a gap in derstanding the
unique challenges these confined settings pose for autonomous navigation
systems.
This study tackles indoor autonomous driving, particularly in overlooked
spaces like underground parking lots. Using CARLA's simulation platform, a
realistic parking model is created for data gathering. An occupancy grid
network then processes this data to predict vehicle paths and obstacles,
enhancing the system's perception in complex indoor environments. Ultimately,
this strategy improves safety in autonomous parking operations. The paper
meticulously evaluates the model's predictive capabilities, validating its
efficacy in the context of underground parking. Our findings confirm that the
proposed strategy successfully enhances autonomous vehicle performance in these
complex indoor settings. It equips autonomous systems with improved adaptation
to underground lots, reinforcing safety measures and dependability. This work
paves the way for future advancements and applications by addressing the
research shortfall concerning indoor parking environments, serving as a pivotal
reference point.

本研究通过使用 CARLA 的仿真平台，创建了基于实际情况的停车场模型以收集数据，然后用占据格网络处理该数据以预测车辆路径和障碍物，从而增强自主驾驶系统在复杂室内环境的感知能力，最终提高自主停车操作的安全性。研究结果表明，所提出的策略成功地提升了自主车辆在这些复杂室内环境中的性能，为未来进一步的发展和应用奠定了基础，并弥补了关于室内停车环境的研究不足，成为一个重要的参考点。