Do They Want to Cross? Understanding Pedestrian Intention for Behavior Prediction.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Driving in urban traffic requires making quick and safe decisions while interacting with multiple pedestrians and other road users. Early anticipation of others' intentions is especially important for predicting their future behavior. In this work, we explore the human ability to estimate intentions of pedestrians in typical urban traffic conditions. Towards this goal, we analyze the results of our large-scale experiment that involved over 700 subjects to establish a human reference point for the task of pedestrian intention estimation. We determine what visual features correlate with human decisions and the relative difficulty of scenarios and validate our conclusions using a linear logistic model. Furthermore, we propose two models to demonstrate the benefits of using intention for pedestrian trajectory and future crossing action prediction. Our experiments show that an improvement of up to 5 % can be achieved on both tasks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it