Geo-Context Aware Study of Vision-Based Autonomous Driving Models and Spatial Video Data
arXiv:2109.10895 · doi:10.1109/TVCG.2021.3114853
Abstract
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.
arXiv comment11 pages, 8 figures, and 1 table. This paper is accepted and to be published in IEEE Transactions on Visualization and Computer Graphics
References in corpus (8)
- Striving for Simplicity: The All Convolutional Net
- A Survey of Autonomous Driving: Common Practices and Emerging Technologies
- Deep Reinforcement Learning framework for Autonomous Driving
- RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
- Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
- Learning a Driving Simulator
- A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks
- RNNbow: Visualizing Learning via Backpropagation Gradients in Recurrent Neural Networks