Towards a Benchmark for Trajectory Prediction of Autonomous Vehicles
Bibliographic record
Abstract
The technology stack of connected and autonomous vehicles (CAV) consists of sensing, perception, motion prediction, and motion planning Layers. With much success, the sensing and perception layers have been developed. Recently, R&D activities on the prediction of vehicles trajectory have been attracting a lot of attention as it has the potential to increase safety for road users. Trajectory prediction is a significantly more difficult task because it involves capturing historical patterns of vehicle movements that requires an understanding and analysis of unstructured spatial and temporal data at the same time. Datasets that are used for this research are typically incomplete or not generic enough. Machine learning prediction models are developed in a bit of an ad hoc manner that they use various evaluation metrics. In this paper, we discuss the issues of such datasets, models, and evaluation metrics. We also present the requirements and initial high-level design of a benchmarking software framework that allows model users to search for and select already developed models, contributed by model developers, that process data collected by dataset contributors, and evaluated by the proposed framework. Further design and development of the proposed framework will ensue.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".