A Posture Recognition System to Track Drivers’ Activities While Driving
Bibliographic record
Abstract
To improve traffic safety, different systems were invented to warn drivers when detecting a conflict. However, drivers may be already aware of such conflicts. In these cases, warning drivers may not effective and may lead drivers to not pay attention to warnings in the case of serious conflicts. The purpose of this study is to develop a novel posture recognition system to detect different driver activities while driving. If drivers are not braking or decelerating in the case of a conflict, they are alerted and notified about the conflict. The proposed system is based on a passive method for measuring bodypostures by using two force sensor arrays to inspect the pressure patterns exhibited in the driver’s seat and backrest. Different sensors' distributions were tested to find the most suitable distribution to improve the accuracy of the recognition. A virtual reality (VR) driving simulation was developed to test the accuracy of recognition in an immersive environment. Experiments were carried out to test the posture recognition accuracy in both realistic and VR settings. The results showed that the system could recognize different postures with high accuracy. Such a system can be used to alert drivers and disengage autonomous mode in the case of self-driving and autonomous vehicles when the situation unsafe.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".