Real-Time Driver Maneuver Prediction Using LSTM
Why this work is in the frame
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Bibliographic record
Abstract
Driver maneuver prediction is of great importance in designing a modern Advanced Driver Assistance System (ADAS). Such predictions can improve driving safety by alerting the driver to the danger of unsafe or risky traffic situations. In this research, we developed a model to predict driver maneuvers, including left/right lane changes, left/right turns and driving straight forward 3.6 seconds on average before they occur in real time. For this, we propose a deep learning method based on Long Short-Term Memory (LSTM) which utilizes data on the driver's gaze and head position as well as vehicle dynamics data. We applied our approach on real data collected during drives in an urban environment in an instrumented vehicle. In comparison with previous IOHMM techniques that predicted three maneuvers including left/right turns and driving straight, our prediction model is able to anticipate two more maneuvers. In addition to this, our experimental results show that our model using identical dataset improved F1 score by 4% and increased to 84%.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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