Modelling the impact of risky cut-in and cut-out manoeuvers on traffic platooning safety with predictability and explainability
Bibliographic record
Abstract
This study investigates the impact of risky lane-changing manoeuvres, specifically risky cut-ins and risky cut-outs, on traffic platooning safety – an aspect often overlooked in previous research. An integrated framework, combining eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), is proposed to analyse 559 risky cut-ins and 319 risky cut-out events extracted from the highD dataset. The results indicate that XGBoost outperforms Random Forest, Support Vector Regressor and Multi-Layer Perceptron models in predicting the safety impact of these manoeuvres. The SHAP explainer enhances model interpretability by identifying key contributing factors and their interactions, addressing the limitations of traditional black-box models. This framework balances predictive accuracy and explainability, offering valuable insights for improving Advanced Driving Assistance Systems (ADAS). By mitigating the risks associated with lane-changing manoeuvres, the findings contribute to safer and more efficient traffic management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".