MSight: An Edge-Cloud Infrastructure-based Perception System for Connected Automated Vehicles
This system addresses safety and efficiency challenges for connected automated vehicles by leveraging roadside sensors to reduce occlusion, though it appears incremental as it builds on existing infrastructure-based approaches.
The paper tackles the problem of robust perception for connected automated vehicles by introducing MSight, an edge-cloud infrastructure-based system that provides real-time detection, localization, tracking, and trajectory prediction with lane-level accuracy and minimal latency.
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.