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.
Most existing driver behavior classification technologies have focused on highways and other primary road infrastructures, despite secondary roads accounting for a disproportionately large number of traffic fatalities worldwide. Compared with highways, secondary roads present greater variability in road geometry, infrastructure quality, and traffic interactions, making driver behavior recognition considerably more challenging. This paper investigates the classification of driver behavior on secondary roads using machine learning techniques. Naturalistic driving data obtained from the publicly available UAH-DriveSet dataset were analyzed using two complementary feature groups describing lane detection and traffic status. Four supervised machine learning algorithms, namely, Logistic Regression (LR), gradient boosting (GB), Random Forest (RF), and Artificial Neural Networks (ANNs), were evaluated to classify driving behavior into three categories: Normal, Aggressive, and Drowsy. The extracted features were first analyzed through statistical profiling and exploratory feature analysis before training and evaluating the classification models. The experimental results show that gradient boosting consistently achieved the highest performance for both feature groups, attaining an overall classification accuracy of approximately 67% while providing balanced precision, recall, and F1-scores across all behavioral classes. Logistic regression and random forest produced competitive but lower performance, whereas the Artificial Neural Network yielded the lowest classification accuracy. 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). By enabling earlier identification of aggressive and drowsy driving behaviors on secondary roads, the proposed approach could support timely driver warnings and safety interventions, potentially reducing accident risk. Furthermore, the findings provide a benchmark for future machine learning models designed for real-world secondary-road environments, where driving conditions are more variable and challenging than on highways.
Delivery riders operating two-wheelers face a disproportionately high risk of road accidents, yet automatic crash detection remains rare among Indian food and grocery delivery platforms. This paper presents a machine learning pipeline for real-time crash and fall detection using only smartphone accelerometer and gyroscope data, without dedicated hardware. We combine two public sensor datasets — a motorcycle-fall dataset collected via an instrumented motorcycle with staged real falls, and a smartphone-based driver-behavior dataset — into a unified corpus of 21 independent trials and 1,788 sliding-window feature vectors spanning four classes: normal riding, hard braking, pothole impact, and crash/fall. A Random Forest classifier, selected over a comparably-performing XGBoost model for its interpretability, is evaluated using trial-grouped 5-fold crossvalidation to prevent leakage from overlapping sliding windows. The model achieves a mean cross-validated accuracy of 65.7% (with high inter-fold variance attributable to limited trial diversity in two minority classes) and, more critically for the target application, a mean recall of 92.3% on the crash/fall class with a false-positive rate of 14.8%. We propose a tiered escalation architecture — an on-device check-in prompt followed by automatic emergency notification — to absorb the false-positive cost while preserving high sensitivity to genuine falls. We report our findings transparently, including data-quality issues discovered during preprocessing and the specific data-scarcity limitations driving evaluation variance, and outline a human-in-the-loop retraining strategy as the direct path to improvement.
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 classification of multimodal driver responses under four controlled weather conditions as a validation case. Electroencephalogram, electrocardiogram, and vehicle signals were recorded for 30 participants, and two leakage-free protocols were adopted: leave-one-subject-out (LOSO) cross-validation and a purged temporal-block cross-validation, with all preprocessing, probability calibration, and weight estimation refitted inside every fold. Under LOSO, CORF achieved an accuracy of 0.356 (chance = 0.25) and a 0.630 macro-average area under the curve (AUC), whereas the originally used random overlapping-window split inflated accuracy to 0.92; the fused adverse-class probability discriminated adverse- from clear-weather windows with an AUC of 0.73. The fusion retains a symmetric reliability-weighting structure across classes, and its moderate symmetry-breaking difficulty emphasis significantly improved the most challenging adverse-weather class over equal-weight fusion (snow F1 +9.8 percentage points, Holm-corrected p < 0.001) at a small, statistically non-significant overall accuracy cost. CORF therefore provides a probability-calibrated, interpretable mechanism for controlling class-specific performance tradeoffs, highlighting the necessity of leakage-free validation in multimodal physiological classification.
As a core interaction in the human-vehicle-road system, automated and refined detection of driving behavior has emerged as a crucial research direction in intelligent transportation systems and advanced driver assistance systems. Traditional post-event monitoring models that rely on manual or sensor-based methods are no longer able to meet the requirements of real-time and accurate risk identification. Therefore, this study proposes the You Only Look Once - Lightweight - BiFPN - ECA (YOLO-LBE) detection method. By integrating ghost convolution and GhostC2f modules to diminish computational complexity, the study employs a weighted bidirectional feature pyramid network, and further embed an ECA module to significantly enhance the precision and stability of driver behavior detection. Experimental findings demonstrate that the improved YOLOv8 model improves mAP@0.5 by 5.3%, FPS by 32.4%, Params by 34.4%, and FLOPs by 33.3% compared to YOLOv5s. 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.