Towards an immersive and safer driving experience using computer vision integrated with encoded vibro-tactile feedback
Bibliographic record
Abstract
This paper claims to set up an immersive, responsive vehicle driving system and mechanism for an assisted driving technology. The purpose is to expand the sensory horizon of humans while driving and is motivated by absence of any such system in real world. The system can control and direct an assembly of electronic devices in real time, through usage of an image acquisition subsystem, an object-recognition and tracking algorithm and a haptic modelling subsystem working in-tandem with the user. The object tracking subsystem operates in real time to determine the current position of a vehicle in front by using a camera and continuously updates it in a live video feed, while also identifying and tracking the moving or stationary vehicle. The haptic system, which is integrated with the tracking system, has been programmed to warn the driver of the potential threats that moving/stationary vehicles may generate. All the subsystems are updated and synchronised with each other in real-time to produce a seamless and smooth transition between frames, facilitating a precise and immersive driving experience for anyone. The high accuracy and robustness of the proposed system makes it a versatile component, which can be integrated in variety of applications for enhancing a person's reality perception.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".