Deep Learning-Based Driving Maneuver Prediction System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many of today's vehicles come equipped with Advanced Driver Assistance Systems (ADAS). Proactive ADAS have the ability to predict short term driving situations. This provides drivers more time to take adequate actions to avoid or mitigate driving risks. In this work, we address the question of predicting drivers' imminent maneuvers before they perform an actual steering operation. The proposed system uses deep recurrent neural networks to fuse the information regarding driver observation actions and the driving environment. With new data labeling methods and effective sequential modeling approaches, the system is able to predict with high accuracy driving maneuvers shortly before the actual steering operations. A set of experiments show that the proposed approach anticipates lane change maneuvers 1.50 seconds before cars start to yaw with an accuracy improved to 90.52% and anticipates turn maneuvers at intersections with green lights 2.53 seconds before cars start to yaw with an accuracy improved to 78.59%. We also show in this work how the system can be adapted for driving proficiency assessment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it