地下停车场中可靠且安全的占据栅格预测研究
Research on Reliable and Safe Occupancy Grid Prediction in Underground Parking Lots
AI总结：
本文针对地下停车场室内自动驾驶安全问题，基于 CARLA 构建仿真数据并采用占据栅格网络预测车辆路径与障碍物，验证了其对感知、可靠性和泊车安全性的提升。
摘要：
在科学技术不断进步的背景下，自动驾驶车辆技术已成为学术界高度关注的焦点。然而，在复杂场景中行驶时，如何保证该技术的安全性和可靠性，仍然是一项持续存在的挑战。尽管大部分自动驾驶研究致力于在城市道路和高速公路等露天环境中进行测试，并对其中发挥作用的众多变量进行细致检验，但地下停车场等封闭室内空间在学术讨论中却在很大程度上被忽视。这种差异凸显出，人们对这些受限环境给自主导航系统带来的独特挑战仍缺乏理解。本研究针对室内自动驾驶，尤其是地下停车场这类被忽视的空间。研究利用 CARLA 仿真平台创建逼真的停车模型以收集数据。随后，占据栅格网络处理这些数据，预测车辆路径和障碍物，从而增强系统在复杂室内环境中的感知能力。最终，该策略提升了自动泊车操作的安全性。论文细致评估了模型的预测能力，并在地下停车场景中验证了其有效性。研究结果证实，所提出的策略成功提升了自动驾驶车辆在这些复杂室内环境中的性能。它使自主系统能够更好地适应地下停车场，并强化安全措施和可靠性。本工作通过弥补室内停车环境相关研究的不足，为未来的进展和应用铺平道路，并提供关键参考。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
