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

2025-04-29

AI Summary

This work addresses the limited environmental perception capability of low-penetration floating car observers (FCOs) in intelligent transportation systems. We propose a physics-informed, data-driven FCO detection enhancement framework. Methodologically, we introduce a novel neural-network-accelerated fast simulation paradigm integrating 2D ray tracing, SUMO-ROS/Carla co-simulation, PointPillars-based 3D object detection, LSTM-based temporal modeling, and vehicle re-identification. Crucially, we devise a temporal enhancement strategy to accurately recover temporarily occluded targets. Experiments demonstrate that, at a 20% FCO penetration rate, LiDAR-based floating car data (FCD) achieves a 65% detection coverage; incorporating temporal modeling elevates vehicle recovery rate beyond 80% and significantly reduces localization error. The framework delivers a lightweight, real-time, and robust solution for traffic state perception—enabling scalable digital twin traffic network deployment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Low Level & Physics-based Vision
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Modeling FCO detections for ITS applications in traffic simulations
• Emulating high-fidelity FCO detections using neural networks
• Assessing FCO data impact on traffic state estimation

Innovation

• Integrates onboard sensors for traffic detection
• Uses neural network to emulate high-fidelity simulations
• Employs LiDAR for vehicle identification in traffic