14 citations · 28 across the 6 of their papers we have counts for
6 papers
Periphery-Fovea Multi-Resolution Driving Model guided by Human Attention
Ye Xia, Jinkyu Kim, John Canny +2
Inspired by human vision, we propose a new periphery-fovea multi-resolution driving model that predicts vehicle speed from dash camera videos. The peripheral vision module of the m…
Fast Recurrent Fully Convolutional Networks for Direct Perception in Autonomous Driving
Yiqi Hou, Sascha Hornauer, Karl Zipser
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 ob…
Predicting Driver Attention in Critical Situations
Ye Xia, Danqing Zhang, Jinkyu Kim +3
Robust driver attention prediction for critical situations is a challenging computer vision problem, yet essential for autonomous driving. Because critical driving moments are so r…
Learning to Roam Free from Small-Space Autonomous Driving with A Path Planner
Sascha Hornauer, Karl Zipser, Stella X. Yu
Modern autonomous driving algorithms often rely on learning the mapping from visual inputs to steering actions from human driving data in a variety of scenarios and visual scenes.…
MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving
Sauhaarda Chowdhuri, Tushar Pankaj, Karl Zipser
Autonomous driving requires operation in different behavioral modes ranging from lane following and intersection crossing to turning and stopping. However, most existing deep learn…
Node Specificity in Convolutional Deep Nets Depends on Receptive Field Position and Size
Karl Zipser
In convolutional deep neural networks, receptive field (RF) size increases with hierarchical depth. When RF size approaches full coverage of the input image, different RF positions…