MSight: An Edge-Cloud Infrastructure-based Perception System for Connected Automated Vehicles
Zhang, Rusheng, Meng, Depu, Shen, Shengyin, Zou, Zhengxia, Li, Houqiang, Liu, Henry X.
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Oct-8-2023
- Country:
- South America > Venezuela
- Barinas State > Barinas (0.04)
- North America > United States
- Pennsylvania (0.04)
- Wisconsin > Dane County
- Madison (0.04)
- Michigan > Washtenaw County
- Ann Arbor (0.49)
- Europe > United Kingdom
- Asia
- South America > Venezuela
- Genre:
- Personal (0.93)
- Industry:
- Information Technology (1.00)
- Automobiles & Trucks (1.00)
- Transportation
- Ground > Road (1.00)
- Infrastructure & Services (0.90)
- Technology:
- Information Technology > Artificial Intelligence
- Vision (1.00)
- Robots > Autonomous Vehicles (1.00)
- Representation & Reasoning (1.00)
- Machine Learning
- Neural Networks > Deep Learning (0.68)
- Performance Analysis > Accuracy (0.47)
- Information Technology > Artificial Intelligence