Driving Maneuver Classification: A Comparison of Feature Extraction Methods
Bibliographic record
Abstract
Driving maneuver classification has received increasing attention in recent years. Early work focused on car-based sensor systems, but recently the use of smartphone-based sensors has been increasingly favored. For a driving maneuver classification system, feature extraction often plays an important role. Previous studies have proposed various feature extraction methods for classifying driving maneuvers, however, a direct comparison of feature extraction methods using various data sets is missing. In this paper, we systematically compare three window-based feature extraction methods for driving maneuver classification: statistical values and automatically extracted features using principal component analysis and stacked sparse auto-encoders. Specifically, all sensor information from each data set is first segmented into windowed signals after preprocessing. Then, the three feature extraction methods are applied to those windowed signals. Finally, extracted features are fed into a random forest classifier. Maneuver classification performance is evaluated on three different data sets, demonstrating weighted classification F1-scores of 68.56%, 80.87%, and 87.26%. For all three data sets, statistical features achieve the best performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".