Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection

Li, Shenglin; Yoon, Hwan-Sik

doi:10.3390/s23104888

Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection

Journal Article · Fri May 19 00:00:00 EDT 2023 · Sensors

DOI:https://doi.org/10.3390/s23104888· OSTI ID:1974483

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Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associate these predictions with sensor measurements. Finally, vehicle tracking is accomplished by merging kinematic information from predictions and measurements through the Kalman filter. A case study conducted at an intersection demonstrates the effectiveness of the proposed sensor fusion method for traffic detection and tracking, including performance comparisons with individual sensors.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: EE0009210

OSTI ID:: 1974483

Alternate ID(s):: OSTI ID: 2418084

Journal Information:: Sensors, Journal Name: Sensors Journal Issue: 10 Vol. 23; ISSN SENSC9; ISSN 1424-8220

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

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