TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
Autonomous vehicle technology has been developed in the last decades with
recent advances in sensing and computing technology. There is an urgent need to
ensure the reliability and robustness of autonomous driving systems (ADSs).
Despite the recent achievements in testing various ADS modules, little
attention has been paid on the automated testing of traffic light detection
models in ADSs. A common practice is to manually collect and label traffic
light data. However, it is labor-intensive, and even impossible to collect
diverse data under different driving environments.
To address these problems, we propose and implement TigAug to automatically
augment labeled traffic light images for testing traffic light detection models
in ADSs. We construct two families of metamorphic relations and three families
of transformations based on a systematic understanding of weather environments,
camera properties, and traffic light properties. We use augmented images to
detect erroneous behaviors of traffic light detection models by
transformation-specific metamorphic relations, and to improve the performance
of traffic light detection models by retraining. Large-scale experiments with
four state-of-the-art traffic light detection models and two traffic light
datasets have demonstrated that i) TigAug is effective in testing traffic light
detection models, ii) TigAug is efficient in synthesizing traffic light images,
and iii) TigAug generates traffic light images with acceptable naturalness.
