Extending Operational Design Domain for Perception Systems Through Robust Learning
Bibliographic record
Abstract
Perception modules in Autonomous Driving Systems (ADS) provide excellent performance in simple, constrained environments but generally have precarious performance in real driving situations with various weather and lighting conditions. Therefore, robust perception performance in new and unanticipated domains is a crucial factor for the large-scale application of autonomous driving, especially when considering unexpected scenarios outside the predefined Operational Design Domain (ODD). This paper proposes a novel approach to extending the ODD for perception modules in ADS through robust learning. The model's robustness is characterized by the anchor data and corresponding perturbation model. The robust learning task is then formulated as a min-max optimization problem conjugated to the perturbation model and a semantically parameter-defined constrained exploration space. The proposed robustify procedures solve the optimization problem in terms of robustness-related batch loss and worst-case loss, which improves the model resilience in multiple domain shift experiments, including virtual-real and weather changes. This paper presents experimental results that demonstrate the efficacy of robust learning approaches in extending the ODD for perception modules and provides insights into future research directions in this field.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".