SITAR: Evaluating the Adversarial Robustness of Traffic Light Recognition in Level-4 Autonomous Driving
Why this work is in the frame
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Bibliographic record
Abstract
Traffic Light Recognition (TLR) is vital for Autonomous Driving Systems as it supplies critical information at intersections. Modern TLRs leverage camera and geolocation data, incorporating complex pre-(post)-processing steps and multiple deep learning (DL) models for detecting, recognizing, and tracking traffic lights. While the adversarial robustness of standalone DL models has been extensively studied, the robustness of a modern TLR system, i.e., a complex software component with code and DL models, is rarely studied and hence requires research efforts.In this work, we propose a novel testing framework (namely SITAR) targeting TLR modules from a representative Level-4 ADS, such as Baidu Apollo and Autoware. We design a novel adversarial attack loss function to evaluate and improve the adversarial robustness of modern TLR systems. We applied SITAR on Apollo TLR and compared our novel loss function with the state-of-the-art approaches that can effectively attack object detection and image recognition models. SITAR is shown to be effective and our novel loss function performs better than previous SOTAs with a 93% to 100% success rate with a maximum of five-step iteration and eight pixels per perturbation.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it