A traffic sign recognition system is crucial for safely operating an autonomous driving car and efficiently managing road facilities. Recent studies on traffic sign recognition tasks show significant advances in terms of accuracy on several benchmarks. However, they lack performance evaluation in driving cars in diverse road environments. In this study, we develop a traffic sign recognition framework for a vehicle to evaluate and compare deep learning-based object detection and tracking models for practical validation. We collect a large-scale highway image set using a camera-installed vehicle for training models, and evaluate the model inference during a test drive in terms of accuracy and processing time. In addition, we propose a novel categorization method for urban road scenes with possible scenarios. The experimental results show that the YOLOv5 detector and strongSORT tracking model result in better performance than other models in terms of accuracy and processing time. Furthermore, we provide an extensive discussion on possible obstacles in traffic sign recognition tasks to facilitate future research through numerous experiments for each road condition.

Cargo-fall accidents caused by improper cargo loading have frequently occurred on highways in recent years, posing a growing threat to driver safety. Because enforcement typically relies on fixed inspection stations, some drivers illegally alter their cargo loads between checkpoints to evade overload inspections, and such changes are rarely secured properly, further increasing the risk of cargo-fall accidents. Current cargo inspection systems, however, are largely limited to labor-intensive manual checks or weight-based sensors that cannot distinguish between items of comparable weight. This paper presents an approach that detects cargo load changes on trucks by comparing depth maps acquired through a stereo vision system across consecutive passes. The proposed approach normalizes the raw depth map to correct for perspective distortion and aligns the current and previous cargo-bed regions through one-dimensional projection matching, enabling accurate comparison independent of cargo color or texture. We validate the approach through field experiments at SMTB under two test scenarios, one with uniform cargo items and one with diverse, irregularly placed items, and benchmark it against four widely used alignment algorithms: SIFT, ORB, AKAZE, and XFeat. The proposed algorithm reaches a combined detection rate of 91.49% (43/47), well above every conventional algorithm, at a processing speed nearly on par with ORB, the fastest of the compared methods. Notably, all four conventional algorithms consistently miss the same subset of events involving a tarpaulin, whose color and texture closely resemble the cargo bed, whereas the depth-based approach detects these events without difficulty. These findings offer practical guidance for building more reliable, real-world cargo load change detection systems.