Transformer-Based Model for Predicting Trajectories in Autonomous Vehicle-Pedestrian Conflicts: A Proactive Approach to Road Safety
Bibliographic record
Abstract
Accurate prediction of pedestrian trajectories is crucial for conflict detection in autonomous vehicle (AV) operations. Existing models like Constant Velocity and Long Short-Term Memory (LSTM) networks have limitations. This paper introduces a Transformer-based model for predicting AV and pedestrian trajectories in urban conflict scenarios. Using attention mechanisms, the model dynamically weights features to improve predictions. Trained on the nuPlan dataset with over 1,500 hours of driving data, the Transformer model outperformed Constant Velocity and LSTM models across prediction horizons at three seconds. In Boston, it achieved an Average Displacement Error (ADE) of 2.071 meters for AV prediction, compared to 5.892 meters for Constant Velocity and 6.769 meters for LSTM. The model accurately predicts evasive actions; identifying 62.2% of AVs would accelerate and 41.6% would swerve in response to pedestrians in Boston. In Singapore, Transformers accurately predicted that pedestrians often swerve and accelerate, causing AVs to slow down without altering course.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".