A Novel Multimodal Behavior Prediction Method for Automated Vehicles
Bibliographic record
Abstract
Ensuring the safe and effective path planning of automated vehicles in uncertain conditions hinges on the precise and dependable anticipation of future movements of nearby vehicles, coupled with a comprehensive grasp of the surrounding environment. This difficulty escalates significantly in dynamic and complex scenarios, such as unsignalized intersections, where there are no traffic lights to regulate vehicle behavior, and where multiple lanes are not available to infer drivers intentions based on their chosen lane. In this work, we design a novel method based on deep learning to anticipate vehicle behaviors at unsignalized intersections. Our approach accounts for the inherent uncertainty and multimodality of vehicle behavior by generating multiple potential outcomes. Our introduced model combines temporal convolutions with a mixture density layer to achieve this. We further cluster the obtained potential modes into feasible maneuvers, ranking them according to their probabilities. To evaluate the performance of our multimodal behavior prediction model, we conducted comprehensive evaluations using a substantial real-world dataset comprising over 23000 trajectories. The evaluation results underscore the better performance of our behavior prediction approach when compared to various baseline and state-of-the-art models.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".