7 papers
Rainy screens: Collecting rainy datasets, indoors
Horia Porav, Valentina-Nicoleta Musat, Tom Bruls +1
Acquisition of data with adverse conditions in robotics is a cumbersome task due to the difficulty in guaranteeing proper ground truth and synchronising with desired weather condit…
Don't Worry About the Weather: Unsupervised Condition-Dependent Domain Adaptation
Horia Porav, Tom Bruls, Paul Newman
Modern models that perform system-critical tasks such as segmentation and localization exhibit good performance and robustness under ideal conditions (i.e. daytime, overcast) but p…
Generating All the Roads to Rome: Road Layout Randomization for Improved Road Marking Segmentation
Tom Bruls, Horia Porav, Lars Kunze +1
Road markings provide guidance to traffic participants and enforce safe driving behaviour, understanding their semantic meaning is therefore paramount in (automated) driving. Howev…
Imminent Collision Mitigation with Reinforcement Learning and Vision
Horia Porav, Paul Newman
This work examines the role of reinforcement learning in reducing the severity of on-road collisions by controlling velocity and steering in situations in which contact is imminent…
I Can See Clearly Now : Image Restoration via De-Raining
Horia Porav, Tom Bruls, Paul Newman
We present a method for improving segmentation tasks on images affected by adherent rain drops and streaks. We introduce a novel stereo dataset recorded using a system that allows…
The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping
Tom Bruls, Horia Porav, Lars Kunze +1
Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view. Hence, Inverse Perspective Mapping (I…