Abstract Automated Driving Systems (ADS) open up a new domain for the automotive
industry and offer new possibilities for future transportation with higher efficiency and …

Active learning is a label-efficient machine learning method that actively selects the most
valuable unlabeled samples to annotate. Active learning focuses on achieving the best …

Driving in adverse weather conditions is a key challenge for autonomous vehicles (AV).
Typical scene perception models perform poorly in rainy, foggy, snowy, and cloudy …

Street view imagery (SVI) is instrumental for sensing urban environments, benefitting
numerous domains such as urban morphology, health, greenery, and accessibility. Billions …

Surround-view cameras are a primary sensor for automated driving, used for near-field
perception. It is one of the most commonly used sensors in commercial vehicles primarily …

Pedestrian Detection is the most critical module of an Autonomous Driving system. Although
a camera is commonly used for this purpose, its quality degrades severely in low-light night …

Scaling the distribution of automated vehicles requires handling various unexpected and
possibly dangerous situations, termed corner cases (CC). Since many modules of …

Abstract Bird's-eye-view (BEV) grid is a common representation for the perception of road
components, eg, drivable area, in autonomous driving. Most existing approaches rely on …

Optical flow estimation is a well-studied topic for automated driving applications. Many
outstanding optical flow estimation methods have been proposed, but they become …

The Light Detection and Ranging (LiDAR) sensor has become essential to achieving a high
level of autonomous driving functions, as well as a standard Advanced Driver Assistance …