# Floating Car Observers in Intelligent Transportation Systems: Detection Modeling and Temporal Insights

arXiv: https://arxiv.org/abs/2505.02845 · HTML: https://mlsanity.com/paper/2505.02845/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [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)
- [Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection](https://mlsanity.com/paper/2112.04628/) (2021)
- [Temporal Enhanced Floating Car Observers](https://mlsanity.com/paper/2403.03825/) (2024)

## Built on by (most cited first, 1 of 1)

- [ROSA-RL: Uncertainty-Aware Roundabout Optimized Speed Advisory with Reinforcement Learning](https://mlsanity.com/paper/2606.16558/) (2026)

## Abstract (arXiv)

Floating Car Observers (FCOs) extend traditional Floating Car Data (FCD) by integrating onboard sensors to detect and localize other traffic participants, providing richer and more detailed traffic data. In this work, we explore various modeling approaches for FCO detections within microscopic traffic simulations to evaluate their potential for Intelligent Transportation System (ITS) applications. These approaches range from 2D raytracing to high-fidelity co-simulations that emulate real-world sensors and integrate 3D object detection algorithms to closely replicate FCO detections. Additionally, we introduce a neural network-based emulation technique that effectively approximates the results of high-fidelity co-simulations. This approach captures the unique characteristics of FCO detections while offering a fast and scalable solution for modeling. Using this emulation method, we investigate the impact of FCO data in a digital twin of a traffic network modeled in SUMO. Results demonstrate that even at a 20% penetration rate, FCOs using LiDAR-based detections can identify 65% of vehicles across various intersections and traffic demand scenarios. Further potential emerges when temporal insights are integrated, enabling the recovery of previously detected but currently unseen vehicles. By employing data-driven methods, we recover over 80% of these vehicles with minimal positional deviations. These findings underscore the potential of FCOs for ITS, particularly in enhancing traffic state estimation and monitoring under varying penetration rates and traffic conditions.