Automatic Classification of Road-User Travel Modes in a Mixed Traffic Roundabout
Bibliographic record
Abstract
The objective of this paper is to present and evaluate an automated road-users classification procedure. The classification is based on the analysis of the motion pattern attributes associated with the trajectories of each road-user type; vehicles, pedestrians and cyclists. A novel approach for features selection is proposed where singular spectrum analysis identifies the main harmonics (speed variation) characterizing the movements trajectories. A constraint-based decision procedure is then applied on the selected features to categorize the road-users. Performance evaluation of the proposed classification is presented. Validation of the procedure is undertaken using real world data set collected at a newly designed mixed traffic roundabout in Greater Vancouver, British Columbia. Satisfactory results were demonstrated and evaluated through performance measures with a reported classification accuracy of around 80 percent. The goal of this research is to improve the understanding of road-users behavior in order to enhance the riding condition and provide an efficient and safe commuting environment. The main benefit of this research is to apply classification as a first step in the activity and behavior recognition of road-users in traffic scenes.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".