Triboelectric nanogenerator sensors for intelligent steering wheel aiming at automated driving
Bibliographic record
Abstract
This paper reports a novel intelligent steering wheel developed based on the concept of triboelectricity aiming at automated driving to reduce traffic accidents . A sandwich-type sensor is designed to be integrated into the steering wheel with the aim of identifying driver’s steering intention. The steering wheel of a vehicle is furnished with a triboelectric nanogenerator (TENG)-based sensor for detecting driver intention. The superiority of the TENG-based sensor is demonstrated by comparing it to other available sensors within a vehicle. By employing different machine learning techniques , we develop classification models based on driving data from multiple drivers. We show that the faster reaction time of the TENG-based sensor can aid in emergency obstacle avoidance when compared to the regular steering wheel sensor through the use of model-predictive control. The fusion of data generated by the proposed TENG-based sensor and advanced control model represents a crucial step towards the development of an intelligent steering wheel for automated systems. This will improve the human–machine interaction for vehicle control, ultimately resulting in more efficient and effective control of the vehicle.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".