Predictive Model of Driver's Eye Fixation for Maneuver Prediction in the Design of Advanced Driving Assistance Systems
Bibliographic record
Abstract
Over the last few years, Advanced Driver Assistance Systems (ADAS) have been shown to significantly reduce the number of vehicle accidents. Accord- ing to the National Highway Traffic Safety Administration (NHTSA), driver errors contribute to 94% of road collisions. This research aims to develop a predictive model of driver eye fixation by analyzing the driver eye and head information (cephalo-ocular) for maneuver prediction in an Advanced Driving Assistance System (ADAS). Several ADASs have been developed to help drivers to perform driving tasks in complex environments and many studies were conducted on improving automated systems. Some research has relied on the fact that the driver plays a crucial role in most driving scenarios, recognizing the driver’s role as the central element in ADASs. The way in which a driver monitors the surrounding environment is at least partially descriptive of the driver’s situation awareness. This thesis’s primary goal is the quantitative and qualitative analysis of driver behavior to determine the relationship between driver intent and actions. The RoadLab initiative provided an instrumented vehicle equipped with an on-board diagnostic system, an eye-gaze tracker, and a stereo vision system for the extraction of relevant features from the driver, the vehicle, and the environment. Several driver behavioral features are investigated to determine whether there is a relevant relation between the driver’s eye fixations and the prediction of driving maneuvers.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".