Manned vehicles are maturing into robotics agents under shared control. Vehicle op- erators will be confronted with understanding the expected response from the robotic agent to their actions. I propose a warning system framework to continuously track human inputs, identify possible conflicts and provide contextual warning information to the operator to help avoid accidents. I consider a robotic agent which nominal operation can be encoded using the hybrid automata framework with human inputs. Trajectory prediction methods are used to identify possible conflicts, coded as the avoid set of the system. Using Dynamic Information Flow Tracking (DIFT), human inputs and their effect over time are accumulated and classified in a continuous range between spurious or legitimate inputs.
DIFT, hybrid, control, warning
NSF grant CNS 1017602
Level of Degree
Electrical and Computer Engineering
First Committee Member (Chair)
Second Committee Member
Figueroa, Rafael. "Predictive Warning System for a Class of Shared-Control Vehicular CPS via Dynamic Information Flow Tracking.." (2015). https://digitalrepository.unm.edu/ece_etds/85