Hierarchical Failure Modeling and Machine Learning Assisted Correction of Electro-Mechanical Subsystem Failures in Autonomous Vehicles

Abstract

Autonomous systems that rely on multiple interacting subsystems require a high degree of reliability and resilience to a wide range of failures in those subsystems. In this work the effects of electro-mechanical failures in the steer-by-wire, brake-by-wire and vehicle controller subsystems of autonomous vehicles on subsystem and vehicle level performance are studied. A machine learning assisted correction approach using Gaussian Processes to learn fault dynamics on-line is developed and its efficacy is demonstrated under a variety of vehicle maneuvers and failure conditions at the subsystem and vehicle levels.