CPS Medium: Cooperative Driving in Heterogeneous Traffic of Manned and Unmanned Vehicles

Oklahoma State University, Stillwater OK

Investigators

Abstract

This Cyber Physical Systems (CPS) project will develop a theoretical framework that facilitates safe cooperative driving in heterogeneous traffic of human-operated and autonomously-operated vehicles and demonstrate its feasibility through both simulation and physical experiments. This project will help improve the safety of a transportation system currently being transformed by vehicles with growing autonomous features. By introducing an add-on device, or copilot, into legacy human-driven vehicles, this project will offer a smart driving assistant that is aware of the driver's behaviors and can alert the driver when the vehicle is at risk. When engaged in cooperative driving, the copilot will provide advice that reduces the chance of collision with nearby vehicles. By facilitating cooperative driving for both emerging autonomous vehicles and legacy human-driven vehicles, this project will foster a positive attitude of the public toward autonomous driving, therefore accelerating the adoption of autonomous vehicles into the transportation system. The education and outreach activities will raise more awareness of autonomous driving, Artificial Intelligence (AI) and robotics to the younger generation, and stimulate prospective students to pursue degrees and careers in science and engineering. This research explores the challenging problem of cooperative control of a cyber-physical-human system consisting of both human-operated and autonomously-operated vehicles. First, by leveraging machine learning technologies, this research will develop an integrated data-driven, model-based approach to modeling vehicle driving behaviors with various levels of human and machine control. New machine learning models of human driving behaviors in the presence and absence of copilot's advice will be built, which fuse both external risks and driver's behavior to infer vehicle's intended maneuvers through a novel vehicle reaction model. Second, this research will develop a unified decision framework for cooperative driving that leverages the differences between humans and machines in sensing, analytics, and control to produce real-time advice to autonomous vehicles and drivers for enhanced safety. Such a framework can be extended to other classes of networked cyber-physical-human systems, where intelligent advice to human operators can facilitate collaboration and enhance system level performance and safety. Third, this research will develop both simulated and physical testbeds for experimental evaluation of the theoretical framework. Finally, this research will expand the existing Cooperative Automation Research Mobility Applications platform to both autonomous vehicles and legacy vehicles, which will allow researchers to study new problems in future intelligent transportation systems that involve both humans and machines. 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.