An Interaction-Aware Vehicle Behavior Prediction for Connected Automated Vehicles
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
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it