Lane intrusion detection is an essential component of road safety, as vehicles crossing into lanes without proper signalling can lead to accidents, congestion and traffic violations. In order to overcome these challenges, it has become critical for the future autonomous vehicles and ADAS to possess a precise and reliable lane detection technique which could then further monitor the lane violation in real‐time. However, lane detection is still challenging due to variants in lighting conditions, obstructions and weak markers. This research paper proposes a new YOLOv8 architecture for lane detection and traffic monitoring systems. The modifications considered in the paper are the addition of the large separable kernel attention (LSKA) module and the coordinate attention (CA) mechanism, which enhance the model's feature extraction and its performance in various real‐world scenarios. Furthermore, a new lane intrusion detection (LID) algorithm was created which effectively distinguishes between actual lane intrusions forbidden ones (e.g., crossing solid lane lines) and permissible ones (e.g., crossing dashed lane lines), a crucial aspect for traffic management. The model was successfully tested by transferring the data which was personally recorded on Chinese highways and that show its function in a real environment. The model was tested using a custom dataset which included videos taken on Chinese highways, demonstrating its ability to work under real‐world conditions. In this way, the results show that the proposed YOLOv8 model improves the accuracy and reliability of the lane detection tasks, with the model achieving a mAP of 97.9%, which will be useful and a significant advancement in the application of AI to public safety and highlights the critical role of state‐of‐the‐art deep learning algorithms for enhancing road safety and traffic control.

To address the issues of false positives and missed detections of multi‐scale cracks and small targets in complex environments, this paper proposes an enhanced YOLOv10 instance segmentation network named YOLO‐RCS (YOLOv10s road crack segmentation), specifically designed for segmenting surface cracks. YOLO‐RCS utilizes the DCNv4 module to enhance feature extraction in the backbone network, improving the accurate localization of surface crack segmentation. Additionally, we introduce a novel C3FB structure (an efficient fusion of the C3 module and FocalNextBlock structure) to replace the C2f module in YOLOv10's neck network, aiming to reduce the number of parameters while enhancing model accuracy. Finally, we improve the original loss function to the WIOU loss function, which increases the model's precision and mean average precision (mAP) for segmenting surface cracks. Experimental results show that our model achieves an mAP50 of 90.0% on the surface crack segmentation dataset Crackseg9k, a 5.0% improvement over the original algorithm, with a precision of 91.6%, demonstrating excellent segmentation performance. Compared to some mainstream object detection algorithms, our proposed method also exhibits certain advantages.

Optimizing high-speed railway (HSR) timetables requires coordinated decisions on train stopping patterns and local feasibility constraints, such as headway separation and service ordering, particularly at stations where consecutive trains share infrastructure. As network size and service density increase, station-level interactions can induce strategic behavior among trains competing for overlapping passenger markets. This study introduces a delayed-feedback evolutionary game framework to model interactions between two trains under incomplete information. Here, “delayed feedback” represents the lag in information transmission and periodic strategy updates, rather than operational train delays. We first formulate replicator dynamics for the non-delayed scenario, deriving equilibrium points and local stability conditions. The model is then extended to include delayed feedback in payoff evaluation, and the impact of delay parameters on stability and convergence is analyzed. To account for operational heterogeneity, two station layouts are considered: (i) two tracks per direction, representing small stations with limited overtaking and stop–pass combinations, and (ii) four tracks per direction, representing large stations that allow simultaneous stopping and richer operational patterns. Numerical simulations examine convergence, oscillatory behavior, and parameter sensitivity. Results indicate that delayed feedback significantly influences system dynamics: small delays maintain convergence to evolutionarily stable strategies, whereas larger delays induce persistent oscillations and complex transient trajectories. Station layout further affects stability regions and long-term strategy profiles. This study is based on analytical derivation and numerical simulation rather than sample-based statistical inference; therefore, non-parametric hypothesis testing is not applicable in the present framework. This framework provides a game-theoretic and stability-oriented tool for station-level timetable analysis, offering methodological guidance for timetable design under delayed decision feedback in HSR operations.