MSight: An Edge-Cloud Infrastructure-based Perception System for
  Connected Automated Vehicles
Rusheng Zhang Depu Meng Shengyin Shen Zhengxia Zou Houqiang Li Henry X. Liu
As vehicular communication and networking technologies continue to advance,
infrastructure-based roadside perception emerges as a pivotal tool for
connected automated vehicle (CAV) applications. Due to their elevated
positioning, roadside sensors, including cameras and lidars, often enjoy
unobstructed views with diminished object occlusion. This provides them a
distinct advantage over onboard perception, enabling more robust and accurate
detection of road objects. This paper presents MSight, a cutting-edge roadside
perception system specifically designed for CAVs. MSight offers real-time
vehicle detection, localization, tracking, and short-term trajectory
prediction. Evaluations underscore the system's capability to uphold lane-level
accuracy with minimal latency, revealing a range of potential applications to
enhance CAV safety and efficiency. Presently, MSight operates 24/7 at a
two-lane roundabout in the City of Ann Arbor, Michigan.