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.
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.
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".