A Context-Aware Path Forecasting Method for Connected Autonomous Vehicles
Bibliographic record
Abstract
Forecasting the future paths of surrounding vehicles of a Connected Autonomous Vehicle (CAV) can enhance connectivity and efficiency of vehicular networks, and accurate motion forecasting of nearby vulnerable road users can advance road safety and urban mobility. This task needs a high-level situational awareness for the CAV. Early methods rely solely on vehicle kinematics and overlook the uncertainty within agents behavior and the effects of surrounding context on the behavior of nearby agents, resulting in lower performance or infeasible predictions. In the current work, we introduce a novel context-aware forecasting approach for CAVs that benefits from inverse reinforcement learning (IRL) to condition the future motions of nearby agents on scene-based state sequences defined using a Markov Decision Process. More precisely, we map the images of the surrounding context and the behavior history of agents into rewards and learn optimal expert behaviors using IRL. We validate the path forecasting efficiency of our model using two large motion prediction benchmarks with different scenes and achieve state-of-the-art results in terms of FDE and ADE metrics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".