Abstract
This research addresses the challenge of efficiently processing sensor data across vehicles, edge, and cloud platforms to support resource-intensive perception tasks in autonomous driving.
We investigate distributed processing of perception data from three perspectives to enhance perception accuracy, optimize network and computing resources, and meet real-time latency constraints: (1) offloading computationally intensive tasks to edge or cloud platforms for greater processing capacity and reduced on-board load, (2) evaluating the impact of compression techniques such as H.265 and JPEG on perception quality and transmission latency, and (3) exploring feature-vector transmission as an alternative to raw or compressed data to reduce bandwidth usage while maintaining perception accuracy and minimizing end-to-end delay.
To evaluate performance under realistic conditions, the study utilized both simulation-based assessments and real-world experiments. Using Vehicle-to- Everything (V2X) communication technologies, we analyzed compression and feature-sharing strategies for seamless data exchange between vehicles and infrastructure. Our findings demonstrated that the proposed cooperative and distributed perception strategies significantly improved detection accuracy and reduced processing delays compared to standalone on-board systems.
Raphaël Frank is a Professor / Senior Research Scientist and Entrepreneur at the Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg. He is the Head of the UBIX Research Group, which conducts research on distributed artificial intelligence. Since 2023 he is a member of the University of Luxembourg Council and adjunct Professor at HEC Liège Luxembourg. Since 2024 he is the director of the IPBG ATLAS Industrial PhD Program.
