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

TL;DR

This paper investigates the modeling and application of Floating Car Observers (FCOs) in intelligent transportation systems, demonstrating their effectiveness in vehicle detection and traffic monitoring through advanced simulation and neural emulation techniques.

Contribution

It introduces a neural network-based emulation method for FCO detection modeling and evaluates its impact on traffic state estimation in a digital twin environment.

Findings

FCOs with 20% penetration detect 65% of vehicles across scenarios.

Neural emulation effectively approximates high-fidelity simulations.

Temporal insights enable recovery of over 80% of previously detected vehicles.

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.