用视觉模型提升地下车库的车位感知准确率
Development of Occupancy Prediction Algorithm for Underground Parking Lots
- 基于Transformer的占用网格预测模型，结合车载视角数据
- 在自建数据集SUSTech-COE-ParkingLot上实现高精度感知
- 适合研究自动驾驶在弱光环境下的感知算法者
本研究旨在解决自动驾驶在地下车库等恶劣环境下感知困难的问题。首先，在CARLA仿真环境中构建模拟地下车库场景，并采集符合SemanticKITTI格式的占用真值数据。随后，集成基于Transformer的占用网络模型，完成该场景下的占用网格预测任务。设计了一套完整的鸟瞰图（BEV）感知框架，以提升神经网络在昏暗、复杂环境中的感知精度。最后，通过实验验证了所提方案在地下车库场景下的感知性能。该方案在自建数据集SUSTech-COE-ParkingLot上测试，取得了满意效果。
原文摘要 · Abstract (English)
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.
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