Hybrid-Learning-Based Driver Steering Intention Prediction Using Neuromuscular Dynamics
Bibliographic record
Abstract
The emerging automated driving technology poses a new challenge to driver-automation collaboration, which requires a mutual understanding between humans and machines through their intention identifications. In this article, oriented by human–machine mutual understanding, a driver steering intention prediction method is proposed to better understand human driver's expectation during driver–vehicle interaction. The steering intention is predicted based on a novel hybrid-learning-based time-series model with deep learning networks. Two different driving modes, namely, both hands and single right-hand driving modes, are studied. Different electromyography signals from the upper limb muscles are collected and used for the steering intention prediction. The relationship between the neuromuscular dynamics and the steering torque is analyzed first. Then, the hybrid-learning-based model is developed to predict both the continuous and discrete steering intentions. The two intention prediction networks share the same temporal pattern exaction layer, which is built with the bidirectional recurrent neural network and long short-term memory cells. The model prediction performance is evaluated with a varied history and prediction horizon to exploit the model capability further. The experimental data are collected from 21 participants of varied ages and driving experience. The results show that the proposed method can achieve a prediction accuracy of around 95% steering under the two driving modes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".