Raven: Vision-Based Connected Vehicle Safety Platform Using Infrastructure Sensing, 5G, and MEC

5G cellular communication and Multi-access Edge Computing (MEC) have provided a new opportunity to support connected vehicles safety applications complementing Dedicated Short Range Communication (DSRC) and Cellular Vehicle-to-Everything (C-V2X) direct communication. One major benefit of using cellular networks for connected safety is its wide deployment and support in the vehicles today, and it eliminates the need for additional physical radio technologies in vehicles. However, a few major considerations for cellular networks to support real-time safety applications are latency, reliability of communication, cellular costs, and scalability. 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
$\text{2.5}~m$
,
$\text{4.9}~kph$
(or
$\text{3}~mph$
), and
$2.1^{\circ }$
, respectively, and the driver is warned in-time during all the test runs of the implemented safety applications.