Driver Behavior Assessment in Case of Critical Driving Situations
Bibliographic record
Abstract
Driver behavior assessment is a hard task since it involves distinctive interconnected factors of different types. Especially in case of insurance applications, a trade-off between application cost and data accuracy remains a challenge. Data uncertainty and noises make smart-phone or low-cost sensor platforms unreliable. In order to deal with such problems, this paper proposes the combination between the Belief and Fuzzy theories with a two-level fusion based architecture. It enables the propagation of information errors from the lower to the higher level of fusion using the belief and/or the plausibility functions at the decision step. The new developed risk models of the Driver and Environment are based on the accident statistics analysis regarding each significant driving risk parameter. The developed Vehicle risk models are based on the longitudinal and lateral accelerations (G-G diagram) and the velocity to qualify the driving behavior in case of critical events (e.g. Zig-Zag scenario). In case of over-speed and/or accident scenario, the risk is evaluated using our new developed Fuzzy Inference System model based on the Equivalent Energy Speed (EES). The proposed approach and risk models are illustrated by two examples of driving scenarios using the CarSim vehicle simulator. Results have shown the validity of the developed risk models and the coherence with the a-priori risk assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".