As autonomous vehicles increasingly populate roads, robust testing is essential to ensure their safety and reliability. Due to the limitation that traditional testing methodologies (real-world and simulation testing) are difficult to cover a wide range of scenarios and ensure repeatability, this study proposes a novel virtual-real fusion testing approach that integrates Graph Theory and Artificial Potential Fields (APF) in virtual-real fusion autonomous vehicle testing. Conducted using SUMO software, our strategic lane change and speed adjustment simulation experiments demonstrate that our approach can efficiently handle vehicle dynamics and environmental interactions compared to traditional Rapidly-exploring Random Tree (RRT) methods. The proposed method shows a significant reduction in maneuver completion times—up to 41% faster in simulations and 55% faster in real-world tests. Field experiments at the Vehicle-Road-Cloud Integrated Platform in Suzhou High-Speed Railway New Town confirmed the method’s practical viability and robustness under real traffic conditions. The results indicate that our integrated approach enhances the authenticity and efficiency of testing, thereby advancing the development of dependable, autonomous driving systems. This research not only contributes to the theoretical framework but also has practical implications for improving autonomous vehicle testing processes.

Automatic modulation classification (AMC) is a core task in cognitive radio and spectrum monitoring. Mainstream deep-learning-based AMC models typically contain hundreds of thousands to millions of parameters, making them difficult to deploy on resource-constrained edge devices. This work proposes MicroSSM, a lightweight state-space model for automatic modulation classification. MicroSSM decomposes signal processing into three functionally independent stages: a learnable soft-thresholding module performs adaptive denoising at the input stage; a four-layer small-kernel one-dimensional convolutional neural network (1D CNN) extracts local time–frequency features with a minimal parameter count; and a hierarchical state-space dual-channel module (HSMSSD) performs global temporal modeling with linear complexity via a softmax-normalized discrete state selection mechanism. Unlike Mamba-family models, which rely on continuous-time discretization, HSMSSD selects states through this softmax-normalized discrete choice, thereby avoiding the mapping overhead of continuous-time parameterization on short sequences. The capacities of the three modules are jointly regulated by a unified scaling factor, enabling model variants spanning two orders of magnitude in parameter count. On RML2016.10B, the 2.3K-parameter variant achieves 61.63% accuracy, and scaling to 133.5K parameters raises it to 64.13%; consistent results are observed on RML2016.10A, while a 2D grid extension of MicroSSM reaches 87.86% on the long-sequence, multi-class HisarMod2019.1 dataset. Ablation experiments confirm the effectiveness of each module and show that the marginal benefit of model scaling decays sharply: MicroSSM realizes its accuracy gain within the low-parameter window, and the larger variants are not cost-effective for edge deployment. Finally, MicroSSM is deployed on a Raspberry Pi 4B and achieves sub-millisecond per-sample inference latency over a real 433.92 MHz RF link, demonstrating the feasibility of the model on resource-constrained edge devices.