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Full title—Raven: Vision-Based Connected Vehicle Safety Platform using Infrastructure Sensing, 5G, and MEC
In this paper, we conduct the first real-world implementation and study of using infrastructure-based camera sensing along with 5G cellular communication and MEC computing setup to support connected safety applications.
To achieve this, we design and implement a vision-based multilayer connected safety platform called Raven.
We also develop and evaluate three different connected safety applications namely Intersection T-Bone Vehicle Crash Warning, Vehicle-in-blind-spot Warning and Stopped/Disabled Hazard Vehicle Warning applications on top of the Raven platform. These applications are enabled via a roadside camera that is connected via 5G communication link to the MEC server where Raven detects and tracks vehicles in the received camera frames, estimates each vehicle's dynamics such as tracking ID, position, speed, and heading, packages that information along with other parameters required by the Basic Safety Messages (BSM) standard, and shares it with other vehicles that have subscribed to Raven's service.
Our experiments show promising results: the median error in position, velocity, and heading accuracy is 2.5 m , 4.9 kph (or 3 mph ), and 2.1°, respectively, and the driver is warned in-time during all the test runs of the implemented safety applications.
Full Article: IEEE Transactions on Vehicular Technology, Early Access | 
