Multi-Sensor Data Fusion and Machine Learning for Adaptive Autonomous Systems

Name: Trier Mortlock

Chair: Prof. Mohammad Al Faruque

Date: May 31, 2024

Time:  2 PM

Location: ISEB 1200

Committee: Prof. Faryar Jabbari, Prof. Pramod Khargonekar

Title: Multi-Sensor Data Fusion and Machine Learning for Adaptive Autonomous Systems

Abstract: Autonomous systems rely on sensors to collect data about their surrounding environments to influence their decision-making algorithms.  Sensor fusion involves combining data from different sensors to reduce sensing uncertainties and better understand the environment. Advances in computing platforms and the growing availability of data have ushered a wave of machined learning-based sensor fusion methods that can extend fusion capabilities beyond the current understanding of systems present in mathematical models. However, autonomous systems are increasingly tasked with operating in dynamic settings that require high levels of adaptability, and how best to employ adaptive sensor fusion strategies is a challenging question. This dissertation examines how, where, why, and when to perform multi-sensor data fusion for autonomous systems operating in dynamic environments. A novel sensor fusion architecture that improves autonomous systems’ adaptation capabilities is introduced. The proposed fusion architecture dynamically adapts both how and when fusion is performed by using a machine learning model that learns to identify the optimal sensor fusion strategy for the given sensing context. Use cases across different types of autonomous systems are examined, highlighting the ability of the proposed fusion approach to generalize across various systems and autonomy tasks. Overall, the methodologies and ideas presented in this dissertation can be applied broadly across engineering disciplines and can enhance the development of future technologies that require adaptive sensing capabilities.

Short Bio: Trier Mortlock is a Ph.D. candidate in the Mechanical and Aerospace Engineering program at UC Irvine working under the supervision of Professor Mohammad Al Faruque in the Autonomous and Intelligent Cyber-Physical Systems Lab. Trier obtained his B.S. in Mechanical Engineering from the University of California, Berkeley and his M.S. in Mechanical and Aerospace Engineering from UC Irvine. During his Ph.D., Trier interned at the Frontier Development Lab at the SETI Institute, NASA and was a Doctoral Research Fellow at the Automotive Research Center, a U.S. Army Center of Excellence for Modeling and Simulation of Ground Vehicle Systems.