On the Design and Validation of an Autonomous Vehicle Perception System for the SAE/GM AutoDrive Challenge II
Bibliographic record
Abstract
The University of Toronto Team aUToronto started a new SAE/GM Autodrive Challenge Series II from 2021 to 2025 after a successful four-year run in Series I. This challenge series tasks participating university teams to develop an autonomous vehicle system that can achieve SAE J3016™ Standard Level 4 autonomy by the end of the fourth year and demonstrate its capability at the University of Michigan MCity autonomous vehicle proving ground. The first year of the challenge series asks the team to develop a perception system for an autonomous vehicle and complete detection and tracking tasks for objects commonly seen on the road. The team excelled in all the challenge categories, and we placed first place in all but two competition categories. This thesis describes our winning entry for the Year 1 competition from a Team Principal's system design perspective, and our effort in automating testing and validation for our software system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".