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Found 5 papers, 2 papers with code
Periphery-Fovea Multi-Resolution Driving Model guided by Human Attention
Inspired by human vision, we propose a new periphery-fovea multi-resolution driving model that predicts vehicle speed from dash camera videos.
Fast Recurrent Fully Convolutional Networks for Direct Perception in Autonomous Driving
Deep convolutional neural networks (CNNs) have been shown to perform extremely well at a variety of tasks including subtasks of autonomous driving such as image segmentation and object classification.
Predicting Driver Attention in Critical Situations
Because critical driving moments are so rare, collecting enough data for these situations is difficult with the conventional in-car data collection protocol---tracking eye movements during driving.
MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving
Autonomous driving requires operation in different behavioral modes ranging from lane following and intersection crossing to turning and stopping.
Node Specificity in Convolutional Deep Nets Depends on Receptive Field Position and Size
In convolutional deep neural networks, receptive field (RF) size increases with hierarchical depth.
