Vision-based deep learning (DL) methods have made great progress in learning autonomous driving models from large-scale crowd-sourced video datasets. They are trained to predict instantaneous driving behaviors from video data captured by on-vehicle cameras. In this paper, we develop a geo-context aware visualization system for the study of Autonomous Driving Model (ADM) predictions together with large-scale ADM video data. The visual study is seamlessly integrated with the geographical environment by combining DL model performance with geospatial visualization techniques. Model performance measures can be studied together with a set of geospatial attributes over map views. Users can also discover and compare prediction behaviors of multiple DL models in both city-wide and street-level analysis, together with road images and video contents. Therefore, the system provides a new visual exploration platform for DL model designers in autonomous driving. Use cases and domain expert evaluation show the utility and effectiveness of the visualization system.
翻译:以远见为基础的深层次学习方法在学习大型众源视频数据集的自主驱动模型方面取得了巨大进展,这些方法经过培训,能够预测由车辆摄像机摄取的视频数据产生的即时驱动行为,在本文件中,我们开发了一个地理视频认知系统,用于研究自主驾驶模型的预测以及大型ADM视频数据。通过将DL模型性能与地理可视化技术相结合,视觉研究与地理环境无缝地融合在一起。模型性能措施可以与一套超越地图视图的地理空间特征一起研究。用户还可以发现和比较全市和街道一级分析中多个DL模型的预测行为,以及道路图像和视频内容。因此,该系统为自主驾驶的DL模型设计者提供了一个新的视觉探索平台。使用案例和域专家评价显示了可视化系统的实用性和有效性。