Towards Sustainable Safe Driving: A Multimodal Fusion Method for Risk Level Recognition in Distracted Driving Status
Bibliographic record
Abstract
Precise driving status recognition is a prerequisite for human–vehicle collaborative driving systems towards sustainable road safety. In this study, a simulated driving platform was built to capture multimodal information simultaneously, including vision-modal data representing driver behaviour and sensor-modal data representing vehicle motion. Multisource data are used to quantify the risk of distracted driving status from four levels, safe driving, slight risk, moderate risk, and severe risk, rather than detecting action categories. A multimodal fusion method called vision-sensor fusion transformer (V-SFT) was proposed to incorporate the vision-modal of driver behaviour and sensor-modal data of vehicle motion. Feature concatenation was employed to aggregate representations of different modalities. Then, successive internal interactions were performed to consider the spatiotemporal dependency. Finally, the representations were clipped and mapped into four risk level label spaces. The proposed approach was evaluated under different modality inputs on the collected datasets and compared with some baseline methods. The results showed that V-SFT achieved the best performance with an recognition accuracy of 92.0%. It also indicates that fusing multimodal information effectively improves driving status understanding, and V-SFT extensibility is conducive to integrating more modal data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".