Deep-learning-based semantic segmentation networks typically incorporate object
classification networks in their backbone. This leads to a loss of context because …

This paper provides a review of the literature in on-road vision-based vehicle detection,
tracking, and behavior understanding. Over the past decade, vision-based surround …

Solving tasks for autonomous road vehicles using computer vision is a dynamic and active
research field. However, one aspect of autonomous transportation has received little …

One of the fundamental challenges in the design of perception systems for autonomous
vehicles is validating the performance of each algorithm under a comprehensive variety of …

Deep learning has considerably improved semantic image segmentation. However, its high
accuracy is traded against larger computational costs which makes it unsuit-able for …

Autonomous vehicles could improve mobility, safety, and inclusion in traffic. While this
technology seems within reach, its successful introduction depends on the intended user's …

Semantic scene understanding is crucial for robust and safe autonomous navigation,
particularly so in off-road environments. Recent deep learning advances for 3D semantic …

Perception is the fundamental task of any autonomous driving system, which gathers all the
necessary information about the surrounding environment of the moving vehicle. The …

Using deep learning, 3D autonomous driving semantic segmentation has become a well-
studied subject, with methods that can reach very high performance. Nonetheless, because …

Bird's eye view (BEV) semantic segmentation plays a crucial role in spatial sensing for
autonomous driving. Although recent literature has made significant progress on BEV map …