Perception and Computational Efficiency for Autonomous Vehicles, 28/06/2022

Marilyn Wolf, University of Nebraska — Lincoln

Abstract

Perception is a critical computational task in autonomous vehicles.  Autonomous vehicles place stringent and somewhat conflicting demands on perception systems: high accuracy, low latency, and performance on limited computational resources.  The conflict between these requirements is particularly acute in the case of unmanned aerial vehicles (UAVs) but is also true of ground vehicles.  This talk will describe two related efforts to improve and manage efficiency in perception for autonomous vehicles. Work with Krishna Muuva and Justin Bradley of UNL looks at UAV-UAV tracking.  We show that tracking performance saturates above a given level of perceptual accuracy.  Work with Deep Samal and Saibal Mukhopodhyay of Georgia Tech compares the results of multiple evaluators to improve the accuracy of LIDAR tracking.

Biography

Marilyn Wolf is Elmer E. Koch Professor of Engineering and Director of the School of Computing at the University of Nebraska – Lincoln.  She received her BS, MS, and PhD in electrical engineering from Stanford University in 1980, 1981, and 1984, respectively.  She was with AT&T Bell Laboratories from 1984 to 1989.  She was on the faculty of Princeton University from 1989 to 2007 and was Farmer Distinguished Chair at Georgia Tech from 2007 to 2019.   Her research interests include cyber-physical systems, embedded computing, embedded video and computer vision, and VLSI systems. She has received the IEEE Kirchmayer Graduate Education Award, the IEEE Computer Society Goode Memorial Award, the  ASEE Terman Award, and the IEEE Circuits and Systems Society Education Award. She is a Fellow of the IEEE and ACM and an IEEE Computer Society Golden Core member.