TL;DR
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.
This research establishes a statistically robust and deployable foundation for next-generation intelligent transportation systems by coupling Bayesian learning theory with edge computing design.
The obtained results demonstrate the effectiveness of ensemble learning methods for driver behavior recognition under secondary-road conditions and highlight their potential for integration into intelligent driver monitoring and Advanced Driver Assistance Systems (ADASs).
A system for Real-time anomaly detection and trip rating in taxi driving that uses vehicle telemetry data such as speed, acceleration, yaw rate, steering angle, and GPS, which allows fair and consistent safety evaluation and helps fleet operators monitor and improve service quality.
This paper presents a machine learning pipeline for real-time crash and fall detection using only smartphone accelerometer and gyroscope data, without dedicated hardware, and proposes a tiered escalation architecture to absorb the false-positive cost while preserving high sensitivity to genuine falls.
Multimodal classification often suffers from recognition reliability that is asymmetric across data sources and classes, and its evaluation is frequently complicated by information leakage from overlapping sampling windows. This paper proposes a class-wise-optimized reliability fusion model (CORF), using the classifica...
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.
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