Driver behavior assessment based on loosely coupled GPS/INS integration in harsh environment
Bibliographic record
Abstract
Commercial location-based services use mainly the Global Positioning System (GPS) receiver data to develop the driver assistance and monitoring systems. Nevertheless, the GPS receiver has a lot of problems, and the important one is the signal availability in harsh environment such as urban canyon, tunnel and bridge. To overcome this drawback, the Inertial Navigation System (INS) comes to aid the GPS receiver using Kalman filtering to compute precise the position, velocity and acceleration in these environments. This paper matches the two complementary areas which are: GPS/INS integration and Driver Behavior Assessment (DBA). In the literature, these two fields have been deeply investigated separately. However, an accurate analysis of the driver behavior requires precise and available data (position, velocity and acceleration) even in harsh environment. This paper presents a new method for driving behavior assessment based on the loosely coupled GPS/INS integration that allows a precise results, especially in case of GPS outages which can be modeled in the driver behavior assessment part. This assessment uses the belief theory, to fuse risk information given from the Driver, Vehicle and Environment entities, and the fuzzy theory to reduce the complexity of the fusion problem. The obtained real test results show good performance of the developed algorithms as well as the risk models. In addition, the presented results show the capability of the belief theory to model the GPS outages and the quality of signals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".