Recent technological advancements in computer vision algorithms and data acquisition
devices have greatly facilitated the research and applications of deep learning-based traffic …

Detecting and locating surrounding vehicles robustly and efficiently are essential
capabilities for autonomous vehicles. Existing solutions often rely on vision-based methods …

Only a few existing works exploit multiple modalities of data for road detection task in the
context of autonomous driving. In this work, a deep learning based approach is developed to …

Road detection is one of the most basic tasks of autonomous driving systems. At present,
researches on this issue mainly take two kinds of data as input, ie, LIDAR point clouds and …

Roadside light detection and ranging (LiDAR) is commonly used to record the traffic data of
the whole intersection scene or road segment in intelligent transportation systems (ITSs) …

In this paper, we propose a two-view fusion based convolutional neural network to estimate
road areas in urban environments with LiDAR point clouds as input only. The proposed …

Robust road detection is a key challenge in safe autonomous driving. Recently, with the
rapid development of 3D sensors, more and more researchers are trying to fuse information …

Vehicle detection is one of the most important environment perception tasks for autonomous
vehicles. The traditional vision‐based vehicle detection methods are not accurate enough …

The stable detection and tracking of high-speed vehicles on the road by using LiDAR can
input accurate information for the decision-making module and improve the driving safety of …

Technologies in autonomous vehicles have seen dramatic advances in recent years;
however, it still lacks of robust perception systems for car detection. With the recent …