# Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation

arXiv: https://arxiv.org/abs/1906.06789 · HTML: https://mlsanity.com/paper/1906.06789/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2019.

## Built on by (most cited first, 17 of 17)

- [TUMTraf V2X Cooperative Perception Dataset](https://mlsanity.com/paper/2403.01316/) (2024)
- [A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research](https://mlsanity.com/paper/2204.06527/) (2022)
- [InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensors](https://mlsanity.com/paper/2305.00314/) (2023)
- [ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2402.03235/) (2024)
- [TUMTraf Event: Calibration and Fusion Resulting in a Dataset for Roadside Event-Based and RGB Cameras](https://mlsanity.com/paper/2401.08474/) (2024)
- [Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation](https://mlsanity.com/paper/2204.00132/) (2022)
- [A Review of Testing Object-Based Environment Perception for Safe Automated Driving](https://mlsanity.com/paper/2102.08460/) (2021)
- [Real-Time And Robust 3D Object Detection with Roadside LiDARs](https://mlsanity.com/paper/2207.05200/) (2022)
- [CAROM Air -- Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos](https://mlsanity.com/paper/2306.00075/) (2023)
- [A9 Intersection Dataset: All You Need for Urban 3D Camera-LiDAR Roadside Perception](https://mlsanity.com/paper/2306.09266/) (2023)
- [Soar: Design and Deployment of A Smart Roadside Infrastructure System for Autonomous Driving](https://mlsanity.com/paper/2404.13786/) (2024)
- [RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving](https://mlsanity.com/paper/2303.05203/) (2023)
- [Towards Vision Zero: The TUM Traffic Accid3nD Dataset](https://mlsanity.com/paper/2503.12095/) (2025)
- [Cooperative Visual-LiDAR Extrinsic Calibration Technology for Intersection Vehicle-Infrastructure: A review](https://mlsanity.com/paper/2405.10132/) (2024)
- [Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving](https://mlsanity.com/paper/2410.12163/) (2024)
- [RESOLVE: A Multi-Resolution and Multi-Modal Dataset for Roadside Cooperative Perception](https://mlsanity.com/paper/2606.31895/) (2026)
- [SGTA: Scene-Graph Based Multi-Modal Traffic Agent for Video Understanding](https://mlsanity.com/paper/2604.03697/) (2026)

## Abstract (arXiv)

The environmental perception of an autonomous vehicle is limited by its physical sensor ranges and algorithmic performance, as well as by occlusions that degrade its understanding of an ongoing traffic situation. This not only poses a significant threat to safety and limits driving speeds, but it can also lead to inconvenient maneuvers. Intelligent Infrastructure Systems can help to alleviate these problems. An Intelligent Infrastructure System can fill in the gaps in a vehicle's perception and extend its field of view by providing additional detailed information about its surroundings, in the form of a digital model of the current traffic situation, i.e. a digital twin. However, detailed descriptions of such systems and working prototypes demonstrating their feasibility are scarce. In this paper, we propose a hardware and software architecture that enables such a reliable Intelligent Infrastructure System to be built. We have implemented this system in the real world and demonstrate its ability to create an accurate digital twin of an extended highway stretch, thus enhancing an autonomous vehicle's perception beyond the limits of its on-board sensors. Furthermore, we evaluate the accuracy and reliability of the digital twin by using aerial images and earth observation methods for generating ground truth data.