MSight: An Edge-Cloud Infrastructure-based Perception System for Connected Automated Vehicles
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 32 of its 76 references that are in the map. First posted on arXiv 8 Oct 2023.
Builds on
32 of 76 references.
References · 32
- 2017Attention Is All You Needcited by 60,361
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2020End-to-End Object Detection with Transformerscited by 7,414
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2016Gaussian Error Linear Units (GELUs)cited by 3,502
- 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
- 2018YOLOv3: An Incremental Improvementcited by 2,659
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
- 2016YOLO9000: Better, Faster, Strongercited by 1,431
- 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
- 2016Simple Online and Realtime Trackingcited by 868
20 more
- 2022DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETRcited by 573
- 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 530
- 2021Conditional DETR for Fast Training Convergencecited by 424
- 2019Tracking without bells and whistlescited by 382
- 2020Learning Lane Graph Representations for Motion Forecastingcited by 382
- 2018Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networkscited by 289
- 2020Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Netcited by 228
- 2019Video Object Segmentation and Tracking: A Surveycited by 50
- 2022GANet: Goal Area Network for Motion Forecastingcited by 49
- 2021A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Treescited by 26
- 2022PillarGrid: Deep Learning-based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDARcited by 25
- 2020Waymo Public Road Safety Performance Datacited by 24
- 2022Conditional DETR V2: Efficient Detection Transformer with Box Queriescited by 21
- 2022Real-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Camerascited by 13
- 2020A Vision of C-V2X: Technologies, Field Testing and Challenges with Chinese Developmentcited by 11
- 2020Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operationscited by 11
- 2023Robust Roadside Perception: an Automated Data Synthesis Pipeline Minimizing Human Annotationcited by 6
- 2016Gabor Wavelets in Image Processingcited by 2
- 2021Optimized Object Tracking Technique Using Kalman Filtercited by 2
- 2023ROCO: A Roundabout Traffic Conflict Datasetcited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.