Opportunistic calibration of smartphone orientation in a vehicle
Bibliographic record
Abstract
Modern smartphones are globally ubiquitous. As such, an increasing number of drivers have their smartphone in their vehicle while driving. These phones are equipped with powerful sensing, processing, and communication capabilities. This provides an opportunity to deploy smartphones in modern telematics and mobile telemetry technologies to enable the collection of driving data. Such data can be exploited to obtain insights regarding the vehicle driving patterns as well as the drivers' skills. These insights are valuable in many applications including the usage-based insurance, young driver coaching, and fleet management solutions. However, the sensory data provided by a smartphone must be reoriented with respect to the vehicle to be utilized in such applications. This requires the orientation of the smartphone relative to the vehicle reference system to be estimated through a calibration process. Furthermore, the orientation of a smartphone can vary at any time during a trip due to extraneous factors such as user interaction. This makes the orientation calibration process a challenging task. This paper describes an opportunistic calibration method that continuously monitors a smartphone orientation and compensates for its variation, as necessary. The proposed method relies on the probabilistic fusion of built-in sensors; in particular, the GPS, accelerometer, gyroscope, and magnetometer. The extensive experiments conducted using real-world driving data illustrate the effectiveness of the proposed opportunistic calibration method.
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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 | 0.000 |
| 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".