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 …

Deep learning-based intelligent vehicle perception has been developing prominently in
recent years to provide a reliable source for motion planning and decision making in …

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 …

As autonomous vehicles and autonomous racing rise in popularity, so does the need for
faster and more accurate detectors. While our naked eyes are able to extract contextual …

The open road poses many challenges to autonomous perception, including poor visibility
from extreme weather conditions. Models trained on good-weather datasets frequently fail at …

For autonomous driving, perception is a primary and essential element that fundamentally
deals with the insight into the ego vehicle's environment through sensors. Perception is …

Images captured in low-light conditions present significant challenges for accurate object
detection due to factors such as high noise, poor illumination, and low contrast. In this study …

Video object detection is a fundamental and important task in computer vision. One mainstay
solution for this task is to aggregate features from different frames to enhance the detection …

Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor
savings. Over the past few years, the number of simulators for autonomous driving has …

When deploying segmentation models in practice it is critical to evaluate their behaviors in
varied and complex scenes. Different from the previous evaluation paradigms only in …