CPS: Medium: High Confidence Active Safety Control in Automotive Cyber-Physical Systems
Lead PI:
Francesco Borrelli
Co-PI:
Abstract
The objective of this research is to study the formal design and verification of advanced vehicle dynamics control systems. The approach is to consider the vehicle-driver-road system as a cyber-physical system (CPS) by focusing on three critical components: (i) the tire-road interaction; (ii) the driver-vehicle interaction; and (iii) the controller design and validation. Methods for quantifying and estimating the uncertainty of the road friction coefficient by using self-powered wireless sensors embedded in the tire are developed for considering tire-road interaction. Tools for real-time identification of nominal driver behavior and uncertainty bounds by using in-vehicle cameras and body wireless sensors are developed for considering driver-vehicle interaction. A predictive hybrid supervisory control scheme will guarantee that the vehicle performs safely for all possible uncertainty levels. In particular, for controller design and validation, the CPS autonomy level is continuously adapted as a function of human and environment conditions and their uncertainty bounds quantified by considering tire-road and driver-vehicle interaction. High confidence is critical in all human operated and supervised cyber-physical systems. These include environmental monitoring, telesurgery, power networks, and any transportation CPS. When human and environment uncertainty bounds can be predicted, safety can be robustly guaranteed by a proper controller design and validation. This avoids lengthy and expensive trial and error design procedures and drastically increases their confidence level. Graduate, undergraduate and underrepresented engineering students benefit from this project through classroom instruction, involvement in the research and substantial interaction with industrial partners from the fields of tires, vehicle active safety, and wireless sensors.
Performance Period: 09/01/2009 - 08/31/2012
Institution: University of California-Berkeley
Sponsor: National Science Foundation
Award Number: 0931437