aUToTrack: A Lightweight Object Detection and Tracking System for the\n SAE AutoDrive Challenge
Bibliographic record
Abstract
The University of Toronto is one of eight teams competing in the SAE\nAutoDrive Challenge -- a competition to develop a self-driving car by 2020.\nAfter placing first at the Year 1 challenge, we are headed to MCity in June\n2019 for the second challenge. There, we will interact with pedestrians,\ncyclists, and cars. For safe operation, it is critical to have an accurate\nestimate of the position of all objects surrounding the vehicle. The\ncontributions of this work are twofold: First, we present a new object\ndetection and tracking dataset (UofTPed50), which uses GPS to ground truth the\nposition and velocity of a pedestrian. To our knowledge, a dataset of this type\nfor pedestrians has not been shown in the literature before. Second, we present\na lightweight object detection and tracking system (aUToTrack) that uses\nvision, LIDAR, and GPS/IMU positioning to achieve state-of-the-art performance\non the KITTI Object Tracking benchmark. We show that aUToTrack accurately\nestimates the position and velocity of pedestrians, in real-time, using CPUs\nonly. aUToTrack has been tested in closed-loop experiments on a real\nself-driving car, and we demonstrate its performance on our dataset.\n
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.019 |
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".