Automated Audible Truck-Mounted Attenuator Alerts: Vision System Development and Evaluation
Background: The rise in work zone crashes due to distracted and aggressive driving calls for improved safety measures. While Truck-Mounted Attenuators (TMAs) have helped reduce crash severity, the increasing number of crashes involving TMAs shows the need for improved warning systems. Methods: This...
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| Language: | English |
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MDPI AG
2024-10-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/5/4/90 |
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| author | Neema Jakisa Owor Yaw Adu-Gyamfi Linlin Zhang Carlos Sun |
| author_facet | Neema Jakisa Owor Yaw Adu-Gyamfi Linlin Zhang Carlos Sun |
| author_sort | Neema Jakisa Owor |
| collection | DOAJ |
| description | Background: The rise in work zone crashes due to distracted and aggressive driving calls for improved safety measures. While Truck-Mounted Attenuators (TMAs) have helped reduce crash severity, the increasing number of crashes involving TMAs shows the need for improved warning systems. Methods: This study proposes an AI-enabled vision system to automatically alert drivers on collision courses with TMAs, addressing the limitations of manual alert systems. The system uses multi-task learning (MTL) to detect and classify vehicles, estimate distance zones (danger, warning, and safe), and perform lane and road segmentation. MTL improves efficiency and accuracy, making it ideal for devices with limited resources. Using a Generalized Efficient Layer Aggregation Network (GELAN) backbone, the system enhances stability and performance. Additionally, an alert module triggers alarms based on speed, acceleration, and time to collision. Results: The model achieves a recall of 90.5%, an mAP of 0.792 for vehicle detection, an mIOU of 0.948 for road segmentation, an accuracy of 81.5% for lane segmentation, and 83.8% accuracy for distance classification. Conclusions: The results show the system accurately detects vehicles, classifies distances, and provides real-time alerts, reducing TMA collision risks and enhancing work zone safety. |
| format | Article |
| id | doaj-art-cfcc105d29c84611900ba71f1e91086e |
| institution | DOAJ |
| issn | 2673-2688 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-cfcc105d29c84611900ba71f1e91086e2025-08-20T02:55:35ZengMDPI AGAI2673-26882024-10-01541816183610.3390/ai5040090Automated Audible Truck-Mounted Attenuator Alerts: Vision System Development and EvaluationNeema Jakisa Owor0Yaw Adu-Gyamfi1Linlin Zhang2Carlos Sun3Department of Civil and Environmental Engineering, University of Missouri, Columbia, MO 65211, USADepartment of Civil and Environmental Engineering, University of Missouri, Columbia, MO 65211, USADepartment of Civil and Environmental Engineering, University of Missouri, Columbia, MO 65211, USADepartment of Civil and Environmental Engineering, University of Missouri, Columbia, MO 65211, USABackground: The rise in work zone crashes due to distracted and aggressive driving calls for improved safety measures. While Truck-Mounted Attenuators (TMAs) have helped reduce crash severity, the increasing number of crashes involving TMAs shows the need for improved warning systems. Methods: This study proposes an AI-enabled vision system to automatically alert drivers on collision courses with TMAs, addressing the limitations of manual alert systems. The system uses multi-task learning (MTL) to detect and classify vehicles, estimate distance zones (danger, warning, and safe), and perform lane and road segmentation. MTL improves efficiency and accuracy, making it ideal for devices with limited resources. Using a Generalized Efficient Layer Aggregation Network (GELAN) backbone, the system enhances stability and performance. Additionally, an alert module triggers alarms based on speed, acceleration, and time to collision. Results: The model achieves a recall of 90.5%, an mAP of 0.792 for vehicle detection, an mIOU of 0.948 for road segmentation, an accuracy of 81.5% for lane segmentation, and 83.8% accuracy for distance classification. Conclusions: The results show the system accurately detects vehicles, classifies distances, and provides real-time alerts, reducing TMA collision risks and enhancing work zone safety.https://www.mdpi.com/2673-2688/5/4/90multi-task learningwork zone safetyTruck Mounted Attenuators (TMA)automated audible alertscomputer vision |
| spellingShingle | Neema Jakisa Owor Yaw Adu-Gyamfi Linlin Zhang Carlos Sun Automated Audible Truck-Mounted Attenuator Alerts: Vision System Development and Evaluation AI multi-task learning work zone safety Truck Mounted Attenuators (TMA) automated audible alerts computer vision |
| title | Automated Audible Truck-Mounted Attenuator Alerts: Vision System Development and Evaluation |
| title_full | Automated Audible Truck-Mounted Attenuator Alerts: Vision System Development and Evaluation |
| title_fullStr | Automated Audible Truck-Mounted Attenuator Alerts: Vision System Development and Evaluation |
| title_full_unstemmed | Automated Audible Truck-Mounted Attenuator Alerts: Vision System Development and Evaluation |
| title_short | Automated Audible Truck-Mounted Attenuator Alerts: Vision System Development and Evaluation |
| title_sort | automated audible truck mounted attenuator alerts vision system development and evaluation |
| topic | multi-task learning work zone safety Truck Mounted Attenuators (TMA) automated audible alerts computer vision |
| url | https://www.mdpi.com/2673-2688/5/4/90 |
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