CPS: Small: Robust and Efficient Perception System for Autonomous Vehicles (REPAVE)

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The goal of this project is to advance the science of designing efficient and robust perception systems for complex cyber-physical systems such as autonomous vehicles (AV). Industries in the US, especially AV-related industries, must be competitive in terms of innovations in computer vision and security issues related to deep learning models deployed in complex cyber-physical systems. This project provides these innovations and prepares new generations of computer scientists that can join the industry workforce. The solutions created in this project will ensure safer operations of autonomous systems which benefit the society overall. Finally, hands-on training sessions will be organized at top computer vision conferences to provide opportunities to members of underrepresented groups. This project explores innovative solutions for efficient deep learning models in computer vision which are suitable for resource constrained devices and smarter sensors which can improve the robustness of the perception systems in complex cyber-physical systems. First, a more robust deep learning based perception system utilizing a combination of a color camera and cheaper sensors with lower cost but with better accuracy and efficiency will be created. Second, autonomous systems typically operate under dynamic environments. A more robust decision making module which utilizes context information involving interactions among nearby moving agents to make quick and accurate predictions of their future movements will also be designed. Complex cyber-physical systems are subjected to cyberattacks, and smarter sensors with unique signatures will be designed to allow such perception systems to be more resilient.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.