The recent proliferation of computing technologies (eg, sensors, computer vision, machine
learning, and hardware acceleration) and the broad deployment of communication …

Recently, with the rapid development in vehicle-to-infrastructure communication
technologies, the infrastructure-based, roadside perception system for cooperative driving …

Data from infrastructure sensors can significantly improve the field of view for intelligent
vehicles (IV), both in terms of range and completeness. In the MEC-View project, we …

Infrastructure-assisted autonomous driving is an emerging paradigm that expects to
significantly improve the driving safety of autonomous vehicles. The key enabling …

Existing datasets for autonomous driving (AD) often lack diversity and long-range
capabilities, focusing instead on 360* perception and temporal reasoning. To address this …

Safety is the most important requirement for autonomous vehicles; hence, the ultimate
challenge of designing an edge computing ecosystem for autonomous vehicles is to deliver …

Sensor-based perception on vehicles are becoming prevalent and important to enhance
road safety. Autonomous driving systems use cameras, LiDAR and radar to detect …

Focusing on the task of point-to-point navigation for an autonomous driving vehicle, we
propose a novel deep learning model trained with end-to-end and multi-task learning …

Connected and Autonomous Vehicles (CAVs) heavily rely on 3D sensors such as LiDARs,
radars, and stereo cameras. However, 3D sensors from a single vehicle suffer from two …

In this paper, we propose Chimera, a novel hybrid edge computing framework, integrated
with the emerging edge cloud radio access network, to augment network-wide vehicle …