Continuous Driver Steering Intention Prediction Considering Neuromuscular Dynamics and Driving Postures
Bibliographic record
Abstract
Predicting driver steering intention enables intelligent vehicles to optimize its assistance and collaborative strategies with the human driver in advance, which contribute to an intelligent mutual-understanding system for driver-vehicle collaboration. In this study, a deep time-series learning-enabled driver steering intention prediction system is developed based on the Electromyography (EMG) signal processing. Specifically, the connection between the upper limb EMG signals from different muscles and the steering torque is established using a deep bi-directional long short-term memory (BiLSTM) recurrent neural network (RNN). The deep time-series model is trained to predict the future steering torque with historical EMG signals, and the prediction horizon is selected as 200 ms in this study. Moreover, three different steering postures with different hand positions on the steering wheel are studied. A joint BiLSTM network with shared temporal pattern extraction layers is developed to investigate the impact of the hand positions on the steering intention prediction. It is found that based on the joint BiLSTM network, the most accurate steering intention can be achieved with both hands on 3-clock positions. The experiments are conducted on a driving simulator environment with 21 participants. The proposed system can be used for precise driver steering intention prediction system towards a better mutual-understanding module on the intelligent and automated driving vehicles.
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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 | 0.000 |
| 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".