Currently, self-driving, emerging as a key automatic application, has brought a huge
potential for the provision of in-vehicle services (eg, automatic path planning) to mitigate …

Vehicle-based visual crowdsourcing is an emerging paradigm where the visual data
collected from dash cameras are analyzed with the aim of measuring phenomena of …

Environment perception for autonomous driving traditionally uses sensor fusion to combine
the object detections from various sensors mounted on the car into a single representation of …

Vehicle-to-everything (V2X) communication techniques enable the collaboration between
vehicles and many other entities in the neighboring environment, which could fundamentally …

Advances in perception for self-driving cars have accelerated in recent years due to the
availability of large-scale datasets, typically collected at specific locations and under nice …

Smart-city intersections will play a crucial role in automated traffic management and
improvement in pedestrian safety in cities of the future. They will (i) aggregate data from in …

To simultaneously enable multiple autonomous driving services on affordable embedded
systems, we designed and implemented {\pi}-Edge, a complete edge computing framework …

Autonomous driving demands accurate perception and safe decision-making. To achieve
this, automated vehicles are typically equipped with multiple sensors (eg, cameras, Lidar …

Abstract Vehicle-to-everything (V2X) is a popular topic in the field of Autonomous Driving in
recent years. Vehicle-infrastructure cooperation (VIC) becomes one of the important …

Humans drive in a holistic fashion which entails, in particular, understanding dynamic road
events and their evolution. Injecting these capabilities in autonomous vehicles can thus take …