Trajectory prediction of traffic agents at urban intersections through learned interactions
Why this work is in the frame
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Bibliographic record
Abstract
To navigate a complex urban environment, it is essential for autonomous vehicles to make educated assumptions and accurate predictions of the movement of other traffic agents. Beyond single object tracking, this task involves understanding behavior of other participants and predicting their trajectories. In this paper, we present a data-driven approach to learn the behavior of traffic agents at an intersection by observing several episodes of real-life scenarios captured through a static camera. We develop a feed-forward artificial neural network called the influence-network, which can simultaneously reason over the influence that agents and the environment have on each other. We compare it to an extension of popularly used Dynamic Bayesian Network. Based on data captured at a busy city intersection, we show that our model can predict trajectories of different classes of traffic agents with improved accuracy, and capture higher-level agent behavior.
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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 | 0.000 |
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 it