Deep Neural Network Perception Models and Robust Autonomous Driving Systems
arXiv:2003.08756
Abstract
This paper analyzes the robustness of deep learning models in autonomous driving applications and discusses the practical solutions to address that.
References in corpus (10)
- A Survey of Autonomous Driving: Common Practices and Emerging Technologies
- ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
- Countering Adversarial Images using Input Transformations
- A study of the effect of JPG compression on adversarial images
- Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression
- Improving Adversarial Robustness via Promoting Ensemble Diversity
- Improving Adversarial Robustness of Ensembles with Diversity Training
- Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control
- Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
- Pixel-wise Attentional Gating for Parsimonious Pixel Labeling