{"id":10616,"date":"2024-12-16T17:48:05","date_gmt":"2024-12-16T16:48:05","guid":{"rendered":"https:\/\/www.uni.lu\/research-en\/?post_type=events&p=10616"},"modified":"2025-01-30T10:35:02","modified_gmt":"2025-01-30T09:35:02","slug":"doctoral-defence-faisal-hawlader","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/research-en\/events\/doctoral-defence-faisal-hawlader\/","title":{"rendered":"Doctoral Defence: Faisal HAWLADER"},"content":{"rendered":"\n
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The Doctoral School in Science and Engineering<\/a> is happy to invite you to Faisal HAWLADER\u2019s defence entitled<\/p>\n\n\n\n

\nInfrastructure Assisted Cooperative and Distributed Perception Strategy for Connected Vehicles<\/h2>\n\n\n\n

Supervisor<\/strong>: Assist. Prof Rapha\u00ebl FRANK<\/a><\/p>\n\n\n\n

The research presented in this dissertation addressed the challenge of efficiently processing sensor data across vehicles, edge, and cloud platforms to support resource-intensive perception tasks in autonomous driving.<\/p>\n\n\n\n

We investigated 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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n