Restricted Research - Award List, Note/Discussion Page

Fiscal Year: 2021

279  University of North Texas  (84575)

Principal Investigator: Dr. Qing Yang

Total Amount of Contract, Award, or Gift (Annual before 2011): $ 30,000

Exceeds $250,000 (Is it flagged?): No

Start and End Dates: 2/24/21 -

Restricted Research: YES

Academic Discipline: 130310 - Computer Science & Engineering

Department, Center, School, or Institute: College of Engineering

Title of Contract, Award, or Gift: Fujitsu Edge Computing Research, Dr. Qing Yang - Research on connected and autonomous vehicles

Name of Granting or Contracting Agency/Entity: Toyota MASTER RECORD


Program Title: N/A


SAMs 1.2.1 The Fujitsu Edge Computing Research project focuses on connected and autonomous vehicles (CAW). Autonomous vehicles are heavily reliant upon their sensors to perfect the perception of surrounding environments, however, with the current state of technology, the data which a vehicle uses is confined to that from its own sensors. Data sharing between vehicles and/or edge servers is limited by the available network bandwidth and the stringent real-time constraints of autonomous driving applications. To address these issues, we propose a point cloud feature based cooperative perception framework (F-Cooper) for connected autonomous vehicles to achieve a better object detection precision. Not only will feature based data be sufficient for the training process, we also use the features’ intrinsically small size to achieve real-time edge computing, without running the risk of congesting the network. Our experiment results show that by fusing features, we are able to achieve a better object detection result, around 10% improvement for detection within 20 meters and 30% for further distances, as well as achieve faster edge computing with a low communication delay, requiring 71 milliseconds in certain feature selections. To the best of our knowledge, we are the first to introduce for the purpose of enhancing object detection and making realtime edge computing on inter-vehicle data feasible for autonomous vehicles.

Discussion: No discussion notes


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