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.

翻译：在科技进步的背景下，自动驾驶技术已成为学术界高度关注的研究焦点。然而，在复杂场景中确保该技术的安全性与可靠性仍是一项持续存在的挑战。尽管大量自动驾驶研究专注于露天环境（如城市道路和高速公路）的测试，并对其中涉及的诸多变量进行了细致考察，但地下停车场等封闭室内空间在很大程度上被学术讨论所忽视。这一差异凸显了在理解此类受限环境对自主导航系统构成的独特挑战方面存在的认知空白。本研究针对室内自动驾驶，尤其关注地下停车场等被忽视的空间。利用CARLA仿真平台，构建了真实的停车场模型以采集数据。随后通过占用栅格网络处理这些数据，以预测车辆路径与障碍物，从而增强系统在复杂室内环境中的感知能力。最终，该策略提升了自主泊车操作的安全性。本文细致评估了模型的预测能力，验证了其在地下停车场场景中的有效性。研究结果证实，所提出的策略成功提升了自动驾驶车辆在此类复杂室内环境中的性能。它使自动驾驶系统能更好地适应地下停车场环境，同时强化了安全措施与可靠性。本工作通过弥补室内泊车环境的研究缺口，为未来的技术发展与实际应用铺平了道路，具有重要的参考价值。