CPS: Medium: Safety Assured, Performance Driven Autonomous Vehicles

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Capabilities of autonomous vehicles has surged in the last ten years, propelled by the promise that, in a very near future, commercial self-driving cars will be safe and perform well. Academia is spurring ground-breaking research (e.g., deep learning) and industry is validating software and hardware extensively with millions of miles being driven on the roads and in simulation. Yet, by all accounts - we are still years away from full deployment. One of the primary limitations is the presence of events outside `typical' scenarios. These events range from environmental anomalies (e.g., a swerving car), sensor mistakes (e.g., missed detection of a truck) to security challenges (e.g., remote attacks, spoofing of sensors). These events, while typically rare, reduce reliability of self-driving cars to a level that is unacceptable to the consumer. This research program will develop new algorithms, hardware and validated CPS architecture concepts for autonomous systems operating for long periods of time, such as self-driving cars and flying delivery robots. The work will also be applicable to any autonomous system operating in dynamic environments, such as robots operating in public areas and the home. This research project will develop a holistic CPS architecture for safety assurance and continual performance improvement for autonomous systems operating over long periods of time via probabilistic algorithms, safety guarantees and a secure and agile platform. The technical approach develops three sub-architectures for autonomous CPS systems. A Safety Assured architecture provides probabilistic collision guarantees on secure hardware. A Performance Driven architecture provides robust perception and planning in general conditions, with adaptable algorithms and hardware via dynamic resource allocation. And a Self-Improving architecture works in the background to reason about rare events outside typical scenarios and improve perception and planning algorithms via model learning and software updates. Importantly, by directly working with the inherent coupling between the hardware platform and algorithms, a safety assured CPS architecture will be developed to provide collision avoidance guarantees due to rare events. In addition, adaptive resource allocation on a hardware/software platform along with novel agile algorithms which are adaptable will allow the system to further refine and update inference about the scene and plan options, as well as improve over time. Two experimental testbeds will be used to validate the research. The first is a robot driving in a controlled lab environment in a small-scale city. The second testbed utilizes regularly logged sensor data from a self-driving car to evaluate perception-based mistakes, environmental anomalies, and continual improvement over time.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.