CAREER: Privacy-Aware Collaborative Sensing and Control for Cloud-Enabled Automotive Vehicles

This Faculty Early Career Development Program (CAREER) grant will support research that will contribute novel methodologies related to cloud-enabled automotive vehicles, promoting both the progress of science and advancing transportation safety and efficiency. With the advent of 5G technology, cloud computing is expected to revolutionize automotive applications by providing “big data” and real-time, high-fidelity computing capabilities.

Despite its promise, the use of cloud computing in automotive vehicle control and sensing still has limited success due to concerns in communication privacy and real-time constraints inherent to many automotive systems. This award supports fundamental research that addresses the major challenges in cloud-based control, collaborative sensing, decentralized optimization, and privacy preservation.

The new designs and methodologies will offer a transformative framework in cloud-facilitated collaborative sensing and control that seamlessly integrate cloud and vehicle resources to enable smarter, safer, and greener next-generation automotive systems. This research is synergistic with key societal goals related to developing efficient, secure, and safe transportation systems.

Therefore, results from this research will benefit the U.S. economy and life quality. This research involves several disciplines including control theory, machine learning, vehicle dynamics, and privacy preservation. The multi-disciplinary approach also facilitates the participation of underrepresented groups in research and positively impacts engineering education.

The cloud-facilitated collaborative sensing and control is expected to greatly enhance vehicle control performance, and achieve improved safety, energy efficiency, and ride comfort. In pursuit of this goal, four closely integrated research objectives are planned: 1) Develop a novel privacy-preserving, learning-based collaborative sensing framework to enable the exploitation of multiple heterogeneous vehicles to iteratively improve the estimation of important road information (e.g., black ice and pothole) while preserving privacy; 2) Formalize and synthesize privacy-aware cloud-facilitated control to seamlessly integrate cloud and onboard controls for enhanced performance without leaking vehicle privacy; 3) Develop a computationally-efficient, privacy-preserving decentralized control framework by explicitly exploiting the sparse communication/constraint topologies in connected vehicles, and 4) Evaluate and validate the frameworks through extensive simulations and experiments.

Collectively, advances from these research endeavors are expected to make cloud-based vehicle controls practically viable, and it will create new accuracy-friendly and computationally-efficient privacy mechanisms for time-sensitive dynamical systems.

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.