A Deep Learning-Based Real-Time Vehicle Stop Prediction Method Using Velocity Trend Regression 


Vol. 14,  No. 8, pp. 617-626, Aug.  2025
https://doi.org/10.3745/TKIPS.2025.14.8.617


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  Abstract

Conventional enforcement of illegal parking operates on a post-detection basis, where violations are confirmed only after the driver has exited the vehicle. This reactive approach limits the ability to maintain smooth traffic flow and ensure pedestrian safety. Addressing this issue through an IT-based system presents a significant challenge, as it requires high accuracy in determining whether a vehicle has stopped for parking and when to classify it as being in a ‘parked state.’ Moreover, various stop-related regulations, such as temporary halts before right turns, are stipulated in traffic laws, highlighting the broader applicability and importance of early stop prediction technology in both accident prevention and driver accountability. This study proposes a speed trend regression method to predict vehicle stoppage at an early stage. The system integrates YOLOv8 for object detection and ByteTrack for object tracking. By analyzing vehicle speed trends, it classifies vehicles into stopped, moving, and near-stopped categories. Experimental results, based on five test videos, show a Prediction Detection Performance (PDP) rate of 96% and a precision of 0.875, demonstrating the effectiveness and practical applicability of the proposed approach.

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[IEEE Style]

S. H. Cheon and S. H. Kim, "A Deep Learning-Based Real-Time Vehicle Stop Prediction Method Using Velocity Trend Regression," The Transactions of the Korea Information Processing Society, vol. 14, no. 8, pp. 617-626, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.8.617.

[ACM Style]

Seung Hwan Cheon and Soo Hyung Kim. 2025. A Deep Learning-Based Real-Time Vehicle Stop Prediction Method Using Velocity Trend Regression. The Transactions of the Korea Information Processing Society, 14, 8, (2025), 617-626. DOI: https://doi.org/10.3745/TKIPS.2025.14.8.617.

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