Robust and accurate tracking and localization of road users like pedestrians and cyclists is
crucial to ensure safe and effective navigation of Autonomous Vehicles (AVs), particularly so …

Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle
technology. Image based benchmark datasets have driven development in computer vision …

This paper presents an experimental study on 3D person localization (ie pedestrians,
cyclists) in traffic scenes, using monocular vision and LiDAR data. We first analyze the …

We propose a novel and pragmatic framework for traffic scene perception with roadside
cameras. The proposed framework covers a full-stack of roadside perception pipeline for …

The spatial information provided by 3-D object detection is critical for autonomous driving.
Although visual-based object detection has made significant progress in 2-D/3-D in recent …

We present JRDB, a novel dataset collected from our social mobile manipulator JackRabbot.
The dataset includes 64 minutes of multimodal sensor data including stereo cylindrical 360◦ …

Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which
is regarded as a revolutionary promoter for next-generation transportation systems …

Along with the advancement of light-weight sensing and processing technologies,
unmanned aerial vehicles (UAVs) have recently become popular platforms for intelligent …

Mobile robots are used in modern life; however, object recognition is still insufficient to
realize robot navigation in crowded environments. Mobile robots must rapidly and accurately …

As vehicular communication and networking technologies continue to advance,
infrastructure-based roadside perception emerges as a pivotal tool for connected automated …