AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This research establishes a statistically robust and deployable foundation for next-generation intelligent transportation systems by coupling Bayesian learning theory with edge computing design.
IYOLO, an enhanced YOLOv8-based framework for simultaneous detection and classification of vehicles, drivers, and passengers on highways, aiming to distinguish drivers from passengers and establish one-to-one vehicle-driver associations is proposed.
These findings demonstrate that RF–Bayesian provides a stable, interpretable, and computationally efficient framework for smartphone-based driver behavior classification, with practical relevance for telematics, fleet safety management, driver feedback systems, and intelligent transportation safety applications.
Advanced driver behavior analysis is a revolutionary strategy for improving driving behavior and preventing accidents. With the advancements of technology, it is now possible to capture real-time data on driving behavior, encompassing vehicle characteristics such as speed, acceleration, braking, negligent driving, and...
This research method outperforms existing mainstream models in terms of accuracy, efficiency, and interference tolerance, providing reliable technical support for real-time driving behavior monitoring.
EvoEye is proposed, which identifies the current monitor's errors, generates informative executions accordingly, and updates the monitor through self-evolution to enable effective self-evolution.
Quantitative and qualitative analyses, including state-of-the-art comparison, cross-validation, statistical analysis, and computational complexity evaluation, highlight the system’s accuracy, modularity, and suitability for real-world deployment.
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