地下停车场占用预测算法开发
Development of Occupancy Prediction Algorithm for Underground Parking Lots
AI总结：
本文针对地下车库等昏暗环境中的自动驾驶感知难题，在CARLA中构建仿真车库并采集SemanticKITTI格式数据，结合Transformer占用网络和BEV感知框架，在自建SUSTech-COE-ParkingLot数据集上验证了占用预测的准确性。
摘要：
本研究的核心目标是解决自动驾驶在诸如地下室等不利环境中所面临的感知挑战。首先，本文从地下车库的数据采集入手。在CARLA仿真环境中建立了一个模拟地下车库模型，并在该仿真场景中采集SemanticKITTI格式的占用真值数据。随后，研究整合了一个基于Transformer的Occupancy Network模型，以完成该场景下的占用网格预测任务。设计了一个全面的BEV感知框架，以提高神经网络模型在昏暗、具有挑战性的自动驾驶环境中的准确性。最后，实验验证了所提解决方案在地下室场景中感知性能的准确性。所提解决方案在我们自建的地下停车库数据集SUSTech-COE-ParkingLot上进行了测试，取得了令人满意的结果。
英文摘要：
The core objective of this study is to address the perception challenges faced by autonomous driving in adverse environments like basements. Initially, this paper commences with data collection in an underground garage. A simulated underground garage model is established within the CARLA simulation environment, and SemanticKITTI format occupancy ground truth data is collected in this simulated setting. Subsequently, the study integrates a Transformer-based Occupancy Network model to complete the occupancy grid prediction task within this scenario. A comprehensive BEV perception framework is designed to enhance the accuracy of neural network models in dimly lit, challenging autonomous driving environments. Finally, experiments validate the accuracy of the proposed solution's perception performance in basement scenarios. The proposed solution is tested on our self-constructed underground garage dataset, SUSTech-COE-ParkingLot, yielding satisfactory results.
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。
