TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems

TL;DR

TigAug is an automated data augmentation tool designed to enhance testing and training of traffic light detection models in autonomous driving systems by generating diverse, realistic traffic light images under various conditions.

Contribution

The paper introduces TigAug, a novel augmentation framework that systematically creates diverse traffic light images for testing and improving detection models in autonomous vehicles.

Findings

TigAug effectively tests traffic light detection models.

It efficiently synthesizes diverse traffic light images.

Augmented images maintain acceptable naturalness.

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.