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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. 

Object recognition and depth perception are two tightly coupled tasks that are indispensable for situational awareness. Most autonomous systems are able to perform these tasks by processing and integrating data streaming from a variety of sensors. The multiple hardware and sophisticated software architectures required to operate these systems makes them expensive to scale and operate. This paper implements a fast, monocular vision system that can be used for simultaneous object recognition and depth perception. We borrow from the architecture of a start-of-the-art object recognition system, YOLOv3, and extend its architecture by incorporating distances and modifying its loss functions and prediction vectors to enable it to multitask on both tasks. The vision system is trained on a large database acquired through the coupling of LiDAR measurements with complementary 360-degree camera to generate a high-fidelity labeled dataset. The performance of the multipurpose network is evaluated on a test dataset consisting of a total of 7,634 objects collected on a different road network. When compared with ground truth LiDAR data, the proposed network achieves a mean absolute percentage error rate of 11% on the passenger car within 10 m and a mean error rate of 7% or 9% on the truck within 10 m and beyond 10 m, respectively. It was also observed that adding a second task (depth perception) to the modeling network improved the accuracy of object detection by about 3%. The proposed multipurpose model can be used for the development of automated alert systems, traffic monitoring, and safety monitoring.