An Interaction-Aware Vehicle Behavior Prediction for Connected Automated Vehicles
Bibliographic record
Abstract
Reliably anticipating the future behavior of surrounding vehicles is critical for the safe operation of the Connected Automated Vehicles (CAV) and improves traffic safety. This task requires processing the history and current behavior of a target vehicle and its surrounding vehicles. Nevertheless, this level of situational awareness is challenging due to the limited observability of the ego CAV’s mounted sensors, particularly in unsignalized intersections as an example of a complex scenario. In the current study, we propose an interaction-aware behavior prediction framework for CAVs which takes advantage of vehicular communication technologies to improve the prediction performance at the time of occlusion. With the help of Vehicle-to-Vehicle (V2V) communications, connected vehicles can gain an enriched understanding of the current behavior of the nearby vehicles, leading to an enhanced prediction. We benefit from graph convolutional networks to model the connection between the vehicles. We further analyze the proposed model over a large real-world dataset containing 14867 vehicle trajectories. The results indicate the higher performance of the introduced model against several benchmarks.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".