Metamorphic Testing for Vision-Based Autonomous Driving With Road Traffic Risk Exposure Extrapolation

Zhengmin Jiang, Shunran Zhang, Jia Liu, Huiyun Li, Yi Pan, Jianping Wang

Abstract: Autonomous Driving Systems (ADS) are critical components of Intelligent Transportation Systems (ITS), where vehicle-level reliability has a direct bearing on road traffic safety. Evaluating ADS performance in complex environments remains challenging due to the absence of test oracles and the heavy reliance on deep learning. To address these challenges, this study proposes a novel metamorphic testing framework tailored for vision-based ADS. First, causal inference is employed to extract key environmental factors from high-dimensional observational traffic data, thereby reducing the test space. Second, a multi-objective optimization algorithm integrating causal counterfactual reasoning is developed to quantify the challenges associated with specific combinations of causal factors, enabling cost-effective exploration of test conditions. Third, low-risk source images are systematically transformed into hazardous driving scenes through a fine-tuned diffusion model, allowing ADS evaluation to be guided by metamorphic relations (MRs). Empirical experiments show that the proposed method achieves a higher fault detection ratio than the strongest baseline in four out of five ADS models, with relative gains ranging from 18.1% to 88.9%. Data augmentation experiments further demonstrate that incorporating MR-violating test cases can reduce ADS prediction errors by up to 13.67%, with these benefits preserved in real-world road traffic datasets through domain adaptation. This study highlights a new pathway for validating the reliability of vision-based ADS driven by deep learning, thereby supporting the deployment of safer road transportation. The source code for our methods and baselines is available at https://github.com/SafeDL/AutoMetTest

External IDs:doi:10.1109/tits.2025.3649971