20 citations · 21 across the 2 of their papers we have counts for
8 papers
Learning Accurate and Human-Like Driving using Semantic Maps and Attention
Simon Hecker, Dengxin Dai, Alexander Liniger +1
This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like. To tackle the first issue we exploit semantic and visual maps from HE…
Self-supervised Object Motion and Depth Estimation from Video
Qi Dai, Vaishakh Patil, Simon Hecker +3
We present a self-supervised learning framework to estimate the individual object motion and monocular depth from video. We model the object motion as a 6 degree-of-freedom rigid-b…
Learning a Curve Guardian for Motorcycles
Simon Hecker, Alexander Liniger, Henrik Maurenbrecher +2
Up to 17% of all motorcycle accidents occur when the rider is maneuvering through a curve and the main cause of curve accidents can be attributed to inappropriate speed and wrong i…
Learning Accurate, Comfortable and Human-like Driving
Simon Hecker, Dengxin Dai, Luc Van Gool
Autonomous vehicles are more likely to be accepted if they drive accurately, comfortably, but also similar to how human drivers would. This is especially true when autonomous and h…
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding
Dengxin Dai, Christos Sakaridis, Simon Hecker +1
This work addresses the problem of semantic scene understanding under fog. Although marked progress has been made in semantic scene understanding, it is mainly concentrated on clea…
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
Christos Sakaridis, Dengxin Dai, Simon Hecker +1
This work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related…