Differences in Drivers’ Glance Behavior and Lateral Control Ability during Full-Touch Interaction Mode and Conventional Interaction Mode: A Case Study of Road Experiments
Bibliographic record
Abstract
In recent years, the full-touch human–machine interaction (HMI) mode has gained popularity in the automotive market. However, little research has been conducted on how this interaction mode affects drivers’ glance behavior and lateral control ability. In this study, we evaluated the visual engagement and driving performance of 30 participants while driving two vehicles equipped with either the full-touch interaction mode (FTIM) or the conventional interaction mode (CIM) provided by the original equipment manufacturer (OEM). We found that both air conditioning–related tasks required more visual engagement, longer task completion time, and worse lateral vehicle control under FTIM. Furthermore, the gray correlation analysis demonstrated that FTIM exhibited slightly different disadvantages in the two secondary tasks. In the temperature adjustment task, the correlations of glance behavior and lateral control ability between the two interactive modes were 0.688 and 0.680, respectively. In the airflow adjustment task, the correlations of glance behavior and lateral control ability between the two interactive modes were 0.659 and 0.668, respectively. In addition, this study revealed that driving speed had significant effects on glance behavior and lateral driving performance in both interaction modes. As speed increased, self-adjusting glance behavior was evident in performing the secondary task; however, this behavior could not compensate for the deterioration in lateral driving performance caused by the increased speed. The findings will help improve drivers’ perception of FTIM and provide theoretical guidance for the design development of HMI mode.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".