EAGER: Connected and Automated Vehicle Assessment Platform Using a Crowdsourced Cyber-Physical Reality

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This EArly-concept Grant for Experimental Research (EAGER) project will produce an innovative assessment platform for the evaluation of the impacts of Connected and Automated Vehicles (CAVs) on individuals including drivers, passengers and pedestrians. Since its inception in the early 2000s, CAV technology has been expected to revolutionize the way a vehicle is controlled, potentially having significant implications on safety and efficiency of transportation systems. Existing evaluation approaches for CAVs heavily rely on computer simulations, which are insufficient for capturing the impact on human beings who will have various cognitive and behavioral reactions in response to CAVs. Missing this link, CAVs cannot be deployed on the road. By integrating robotics, 3-D printing, wireless network, traffic sensing and crowdsourcing technologies, the assessment platform that will be built through this EAGER project will reform the existing evaluation paradigm relying on simulations. Combining this platform with CAVs can create a positive feedback loop between design, development, and assessment of CAVs and thus enable its on-road deployment. This research primarily focuses on the assessment of CAV impacts on the individuals who are 1) passengers of CAVs, 2) drivers in the vicinity of CAVs, or 3) pedestrians around CAVs. To seamlessly capture their cognitive (e.g., safety awareness, degree of comfort) and behavioral reactions (e.g., steering maneuvers, accelerating or decelerating activities), this project develops a novel assessment platform utilizing a crowdsourced cyber-physical reality to realize the high-fidelity evaluations of CAVs. Using visual and force feedback, human involvement as drivers, passengers, and pedestrians could be assessed through cyber-physical reality. This EAGER project will address a number of technical challenges including: 1) how to assure that the platform's traffic dynamics would well represent the real-world situations; 2) how to seamlessly connect the human testers with miniature environment through virtual reality; and 3) how to design experimental scenarios to investigate a wide variety of potential problems in CAV technologies. The project will tackle the challenges by integrating robotics, 3D-printing, traffic engineering, and crowdsource technologies. The platform will be validated by comparing the quantitative measures collected from the proposed platform with those predicted by theories and sampled from real world traffic. It is also expected that demonstrations of the built platform will be widely conducted and disseminated at the end of the project.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.