# Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles

arXiv: https://arxiv.org/abs/2007.04350 · HTML: https://mlsanity.com/paper/2007.04350/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligence](https://mlsanity.com/paper/2305.01263/) (2023)
- [A New Era of Mobility: Exploring Digital Twin Applications in Autonomous Vehicular Systems](https://mlsanity.com/paper/2305.16158/) (2023)
- [A survey on deep learning approaches for data integration in autonomous driving system](https://mlsanity.com/paper/2306.11740/) (2023)
- [Learning Based Task Offloading in Digital Twin Empowered Internet of Vehicles](https://mlsanity.com/paper/2201.09076/) (2021)
- [A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation](https://mlsanity.com/paper/2402.01686/) (2024)

## Abstract (arXiv)

With the rapid development of intelligent vehicles and Advanced Driving Assistance Systems (ADAS), a mixed level of human driver engagements is involved in the transportation system. Visual guidance for drivers is essential under this situation to prevent potential risks. To advance the development of visual guidance systems, we introduce a novel sensor fusion methodology, integrating camera image and Digital Twin knowledge from the cloud. Target vehicle bounding box is drawn and matched by combining results of object detector running on ego vehicle and position information from the cloud. The best matching result, with a 79.2% accuracy under 0.7 Intersection over Union (IoU) threshold, is obtained with depth image served as an additional feature source. Game engine-based simulation results also reveal that the visual guidance system could improve driving safety significantly cooperate with the cloud Digital Twin system.