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The detection of a fallen person (FPD) is a crucial task in guaranteeing individual safety. Although deep-learning models have shown potential in addressing this challenge, they face several obstacles, such as the inadequate utilization of global contextual information, poor feature extraction, and substantial computational requirements. These limitations have led to low detection accuracy, poor generalization, and slow inference speeds. To overcome these challenges, the present study proposed a new lightweight detection model named Global and Local You-Only-Look-Once Lite (GL-YOLO-Lite), which integrates both global and local contextual information by incorporating transformer and attention modules into the popular object-detection framework YOLOv5. Specifically, a stem module replaced the original inefficient focus module, and rep modules with re-parameterization technology were introduced. Furthermore, a lightweight detection head was developed to reduce the number of redundant channels in the model. Finally, we constructed a large-scale, well-formatted FPD dataset (FPDD). The proposed model employed a binary cross-entropy (BCE) function to calculate the classification and confidence losses. An experimental evaluation of the FPDD and Pascal VOC dataset demonstrated that GL-YOLO-Lite outperformed other state-of-the-art models with significant margins, achieving 2.4–18.9 mean average precision (mAP) on FPDD and 1.8–23.3 on the Pascal VOC dataset. Moreover, GL-YOLO-Lite maintained a real-time processing speed of 56.82 frames per second (FPS) on a Titan Xp and 16.45 FPS on a HiSilicon Kirin 980, demonstrating its effectiveness in real-world scenarios.

Considering the driver’s sensing the headway and velocity the different time-varying delays exist, respectively, and the sensitivity of drivers changes with headway and speed. Introducing the fuzzy control theory, a new fuzzy car-following model with two delays is presented, and the feedback control strategy of the new fuzzy car-following model is studied. Based on the Lyapunov function theory and linear matrix inequality (LMI) approach, the sufficient condition that the existence of the fuzzy controller is given making the closed-loop system is asymptotic, stable; namely, traffic congestion phenomenon can effectively be suppressed, and the controller gain matrix can be obtained via solving linear matrix inequality. Finally, the simulation examples verify that the method which suppresses traffic congestion and reduces fuel consumption and exhaust emissions is effective.