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 athttps://github.com/SafeDL/AutoMetTest

To enhance the capability of Graph Neural Networks (GNNs) in judging graph isomorphism and graph classification tasks, this paper introduces a metric space-based graph isomorphism judgment method called the k-MSI test, which offers more topological information than the k-WL test and demonstrates superior graph isomorphism judgment capabilities compared to the k-WL test at the same complexity level. On the open test isomorphic dataset BREC, our k-MSI test accuracy rate is more than 11% ahead of the other methods. Furthermore, based on the k-MSI test, we propose a feature enhancement method Node Metric Indicator (NMI) that supplies additional topological information of graphs for GNNs and presents a novel GNN named Metric Space Indicators Graph Neural Network (MSIGNN). Experimental results on a publicly available benchmark graph classification task indicate that the NMI feature-based MSIGNN outperforms state-of-the-art methods on the BREC graph isomorphism test dataset and achieves satisfactory performance on real-world datasets.

Learning-based point cloud completion tasks have shown potential in various critical tasks, such as object detection, assignment, and registration. However, accurately and efficiently quantifying the shape error between the predicted point clouds generated by networks and the ground truth remains challenging. While EMD-based loss functions excel in shape detail and perceived density distribution, their approach can only yield results with significant discrepancies from the actual EMD within a tolerable training time. To address these challenges, we first propose the initial price based on the auction algorithm, reducing the number of iterations required for the algorithm while ensuring the correctness of the assignment results. We then introduce an algorithm to compute the initial price through a successive shortest path and the Euclidean information between its nodes. Finally, we adopt a series of optimization strategies to speed up the algorithm and offer an EMD approximation scheme for point cloud problems that balances time loss and computational accuracy based on point cloud data characteristics. Our experimental results confirm that our algorithm achieves the smallest gap with the real EMD within an acceptable time range and yields the best results in end-to-end training.