Traffic Awareness Through Multiple Mobile Sensor Fusion

An intelligent traffic management system (ITMS) benefits from an accurate and global awareness of the traffic status obtained through traffic data. A growing body of literature recognizes the importance of modern vehicles (MVs) equipped with sensing devices to estimate the traffic data to generate knowledge about the traffic scene. One critical issue is how to use a low-cost sensor mounted on an MV with the purpose of increasing generalizability related to the used sensor and provide the required traffic data. In addition, the sensing coverage of a single vehicle is limited to a specific range. Multiple sensor fusion can be used to overcome these issues. This paper focuses on analyzing target vehicle's geolocation estimated by multiple vehicles equipped with a low-cost monocular camera and proposes a new methodology for fusing those data by considering sensor estimation uncertainties. Our objective is to use the sensor fusion technique to improve the target vehicle's geolocalization estimation accuracy and provide a more comprehensive picture of the traffic status than what can be obtained by using data from only one vehicle. Our proposed methodology includes two primary steps: (1) target vehicle re-identification to determine whether observing vehicles are identifying the same target vehicle, and (2) multiple sensor fusion to dynamically integrate the estimated geolocation of the target vehicle. Our proposed methodology provides one of the first investigations into using data from multiple moving vehicles to estimate and fuse real traffic data. The experiments show that our proposed methodology performs well to enhance geolocation estimation accuracy.

Automated summary

This paper investigates a new methodology for estimating and fusing real-time traffic data using multiple vehicles equipped with low-cost sensors in an intelligent traffic management system. The research shows that the proposed method significantly improves the accuracy of geolocation estimation for target vehicles.