The goal of this project is to bring safety assurance to autonomous and semi-autonomous vehicles. The approach is to lengthen the time that a car can predict its driving path, and share this path with surrounding vehicles. With these expanded predictions, it is possible to estimate the current and future behaviors of vehicles, according to their design models. Currently, online formal safety analysis can promise guarantees and oversight, but overly conservative approaches can lead to bad driving. This is in contrast to the use of test-driving data and machine learning to build driving models, which are difficult to analyze. The project aims to discover the right balance by computationally (1) estimating the current state of the autonomous vehicle and its multi-hop environment from sensor data, (2) predicting vehicle trajectories 4-6 seconds into future, and (3) checking the models and predictions---all in milliseconds. A new scientific workshop will be created to explore similar issues in autonomy, in addition to a new undergraduate course on autonomy.
The project aims to deliver (1) new sensor-fusion algorithms over Vehicle-to-Infrastructure/Vehicle (V2X) systems, (2) a first-of-its-kind open, machine-interpretable library of agent models for driving predictions, (3) algorithms for model identification, and (4) algorithms for checking safety online. These modules will be integrated in an end-to-end system --- OmniVisor --- and evaluated in realistic accident-prone scenarios with real vehicles in University of Michigan's Mcity facility. The research will build connections across the disciplines of formal methods, hybrid dynamical systems, estimation and detection theory, and mobile networking. If successful OmniVisor will provide a scientific basis for obtaining safety assurances for vehicles in mixed-autonomy scenarios and experimentally demonstrate the approach on the road with real vehicles.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
