RMDD: Raspberry Pi-Based Multimodal Dangerous Driving Behavior Detection

Abstract

With the continuous growth in motor vehicle ownership, traffic safety risks caused by dangerous driving behaviors remain an important concern. This paper presents RMDD, a Raspberry Pi-based multimodal dangerous driving monitoring feasibility prototype using YOLO26 visual models, personalized facial fatigue estimation, emotion2vec-based speech recognition, and hierarchical reliability-gated BFV fusion (HRG-BFV). HRG-BFV treats object detection as positive-only support for behavior evidence and scales its contribution by development-fold reliability, thereby avoiding hard rejection when the object detector fails. On locked module tests, behavior classification achieved 96.77% Top-1 accuracy, auxiliary detection reached 0.916 mAP@0.5, personalized facial calibration reduced the false-positive rate from 0.877 to 0.211, and speaker-disjoint speech recognition achieved 0.890 accuracy at 0 dB vehicle noise. The historical fixed BFV baseline achieved 0.601 balanced accuracy and 0.506 macro-F1 on P03–P08 (96 clips). In a stricter three-fold leave-one-external-participant-out evaluation on P04/P05/P08 (48 clips), HRG-BFV achieved 0.726 balanced accuracy, 0.562 macro-F1, 0.833 specificity, and 0.677 ROC-AUC, versus 0.655, 0.469, 0.833, and 0.595 for the historical frozen hard gate on the same cohort. Target support reliability was low (0.097–0.194), so the adaptive term appropriately reverted toward behavior evidence rather than producing an unsupported object detection gain. A sealed P08 behavior test exposed substantial cross-subject degradation (0.320 frame accuracy). A cooled 30 min Raspberry Pi 5 run completed without thermal throttling at 0.927 processing windows/s. The evidence supports controlled prototype feasibility but not population-level or on-road generalization.