Driving style assessment based on the GPS data and fuzzy inference systems
Bibliographic record
Abstract
Car insurance can be computed according to the client's driving behaviour. This option is based on algorithms which use data from the black box. To carry out the monitoring function, the black box integrates an Inertial Navigation System (INS) sensors, a Global Positioning System (GPS), which are Micro Electromechanical Systems (MEMS) based and a flash memory. Researchers were interested in driver behaviour modelling by means of Kalman Filtering (KF) and Hidden Markov Modelling (HMM) and most of them are based only on the driver's acceleration profile during a period of time. This research paper expands the number of parameters, by adding the jerk, to estimate the driver aggressiveness. It proposes two approaches for driving behaviour supervision based on the vehicle velocity signal which is acquired from the Global Positioning System (GPS). The first approach presents three driving behaviour indicators based on the vehicle acceleration and jerk. Simulation results show some weaknesses in terms of driver aggressiveness estimation. For this purpose, the second approach is based on a developed Fuzzy Inference System (FIS) model. The inputs of the FIS model are the vehicle acceleration and jerk and the output remains the aggressiveness score. Experimental results show that the driver aggressiveness is better estimated by the proposed FIS model since the two input parameters are taken into account simultaneously. Furthermore, it will be shown that the denazification techniques and the GPS signal noise have an impact on the driver behaviour estimation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".