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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2209.09020 |
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| _version_ | 1866916173624377344 |
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| author | Ren, Xiaojiang Wang, Yan Geng, Yingfan |
| author_facet | Ren, Xiaojiang Wang, Yan Geng, Yingfan |
| contents | Intelligent Transportation Systems (ITS) have a pressing need for efficient and reliable traffic surveillance solutions. This paper for the first time proposes a surveillance system that utilizes low-cost magnetic sensors for detecting and tracking vehicles continuously along the road. The system uses multiple sensors mounted along the roadside and lane boundaries to capture the movement of vehicles. Real-time measurement data is collected by base stations and processed to produce vehicle trajectories that include position, timestamp, and speed. To address the challenge of tracking vehicles continuously on a road network using a large amount of unlabeled magnetic sensor measurements, we first define a vehicle trajectory tracking problem. We then propose a graph-based data association algorithm to track each detected vehicle, and design a related online algorithm framework respectively. We finally validate the performance via both experimental simulation and real-world road deployment. The experimental results demonstrate that the proposed solution provides a cost-effective solution to capture the driving status of vehicles and on that basis form various traffic safety and efficiency applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_09020 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Vehicle Trajectory Tracking Through Magnetic Sensors: A Case Study of Two-lane Road Ren, Xiaojiang Wang, Yan Geng, Yingfan Robotics Intelligent Transportation Systems (ITS) have a pressing need for efficient and reliable traffic surveillance solutions. This paper for the first time proposes a surveillance system that utilizes low-cost magnetic sensors for detecting and tracking vehicles continuously along the road. The system uses multiple sensors mounted along the roadside and lane boundaries to capture the movement of vehicles. Real-time measurement data is collected by base stations and processed to produce vehicle trajectories that include position, timestamp, and speed. To address the challenge of tracking vehicles continuously on a road network using a large amount of unlabeled magnetic sensor measurements, we first define a vehicle trajectory tracking problem. We then propose a graph-based data association algorithm to track each detected vehicle, and design a related online algorithm framework respectively. We finally validate the performance via both experimental simulation and real-world road deployment. The experimental results demonstrate that the proposed solution provides a cost-effective solution to capture the driving status of vehicles and on that basis form various traffic safety and efficiency applications. |
| title | Vehicle Trajectory Tracking Through Magnetic Sensors: A Case Study of Two-lane Road |
| topic | Robotics |
| url | https://arxiv.org/abs/2209.09020 |