The Center for Education and Research in Information Assurance and Security (CERIAS)

The Center for Education and Research in
Information Assurance and Security (CERIAS)

Integrating Large-Scale Machine Learning and Edge Computing for Collaborative Autonomous Vehicles

Research Areas: Autonomous Systems

Principal Investigator: Dengfeng Sun

 

The research objective of this project is to address the computational challenges in the innovative real-time and intelligent collaborative autonomous vehicles. A novel large-scale machine learning and edge computing framework is developed to integrate the emerging key computational techniques, including fast deep learning optimizations, asynchronous federated learning, cross domain deep learning model compression, hierarchical edge computing, and collaborative autonomous aerial and ground vehicles. Unlike most existing systems that perform big data analysis in central servers or clustering for offline learning, this project provides promising new directions to the real-time analysis of high-throughput sensor data by addressing the critical embedded device data analysis issues including efficiency, scalability, distributed computing, energy saving, and space reduction. The research project combines rigorous theoretical analysis and emerging application studies, and contributes to both academic research and potential commercialized products. Such unique capabilities enable new computational applications in a large number of research areas. It advances and thus extends the relationship between engineering innovation and computational analysis.

Personnel

Other PIs: Joy Wang, Purdue ECE

Students: Bin Du

Representative Publications

Keywords: autonomous vehicles, edge computing, machine learning