SA-SGAN: A Vehicle Trajectory Prediction Model Based on Generative Adversarial Networks
Bibliographic record
Abstract
Vehicle trajectory prediction technology is of great significance in autonomous driving and intelligent transportation systems. Ego-vehicles can judge the future motion state considering nearby vehicles by predicting their trajectories, which facilitates safe and effective decisions to avoid collisions. It is a challenging task to accurately predict the future trajectories of surrounding vehicles. To solve this problem, we propose a Self-Attention Social Generative Adversarial Networks (SA-SGAN) model to predict trajectories of surrounding vehicles. We use the Self-Attention mechanism to capture the correlation between the features in the vehicle trajectory sequence to effectively solve the problem of missing important information due to a long input sequence, and use training characteristic of Generative Adversarial Networks (GAN) to effectively learn the distribution of real trajectory data and improve prediction accuracy. We evaluate the proposed model through NGSIM dataset, use the trained model to investigate the vehicle trajectory in the next 5s in a three-segment scenario of the US-101 highway, and use the Average Displacement Error (ADE) and Final Displacement Error (FDE) as the evaluation indicators. Compared with baseline methods, the proposed model reduces the evaluation indicators to 4.97 and 8.92 respectively.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".