# Cyber Mobility Mirror: A Deep Learning-based Real-World Object Perception Platform Using Roadside LiDAR

arXiv: https://arxiv.org/abs/2202.13505 · HTML: https://mlsanity.com/paper/2202.13505/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 75 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## Builds on (most cited first, 18 of 18)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection](https://mlsanity.com/paper/2103.05346/) (2021)
- [Train in Germany, Test in The USA: Making 3D Object Detectors Generalize](https://mlsanity.com/paper/2005.08139/) (2020)
- [Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections](https://mlsanity.com/paper/2201.07833/) (2022)
- [Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Survey](https://mlsanity.com/paper/2201.11871/) (2022)
- [Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes](https://mlsanity.com/paper/2006.08547/) (2020)
- [Cyber Mobility Mirror for Enabling Cooperative Driving Automation in Mixed Traffic: A Co-Simulation Platform](https://mlsanity.com/paper/2201.09463/) (2022)
- [Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties](https://mlsanity.com/paper/1801.02686/) (2018)

## Built on by (most cited first, 3 of 3)

- [A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation](https://mlsanity.com/paper/2208.10590/) (2022)
- [VINet: Lightweight, Scalable, and Heterogeneous Cooperative Perception for 3D Object Detection](https://mlsanity.com/paper/2212.07060/) (2022)
- [Dual LiDAR-Based Traffic Movement Count Estimation at a Signalized Intersection: Deployment, Data Collection, and Preliminary Analysis](https://mlsanity.com/paper/2507.13073/) (2025)

## Abstract (arXiv)

Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for the next-generation transportation systems. However, the vehicle-based perception may suffer from the limited sensing range and occlusion as well as low penetration rates in connectivity. In this paper, we propose Cyber Mobility Mirror (CMM), a next-generation real-time traffic surveillance system for 3D object perception and reconstruction, to explore the potential of roadside sensors for enabling CDA in the real world. The CMM system consists of six main components: 1) the data pre-processor to retrieve and preprocess the raw data; 2) the roadside 3D object detector to generate 3D detection results; 3) the multi-object tracker to identify detected objects; 4) the global locator to map positioning information from the LiDAR coordinate to geographic coordinate using coordinate transformation; 5) the cloud-based communicator to transmit perception information from roadside sensors to equipped vehicles, and 6) the onboard advisor to reconstruct and display the real-time traffic conditions via Graphical User Interface (GUI). In this study, a field-operational system is deployed at a real-world intersection, University Avenue and Iowa Avenue in Riverside, California to assess the feasibility and performance of our CMM system. Results from field tests demonstrate that our CMM prototype system can provide satisfactory perception performance with 96.99% precision and 83.62% recall. High-fidelity real-time traffic conditions (at the object level) can be geo-localized with an average error of 0.14m and displayed on the GUI of the equipped vehicle with a frequency of 3-4 Hz.