Pavement crack detection is widely spread over road maintenance, ensuring the longevity and safety of infrastructure. Traditional manual inspection methods are time-consuming, labor-intensive, and prone to errors. In response, automated crack detection systems based on deep learning have emerged, offering more efficient and accurate solutions. However, existing models often face challenges such as large model sizes, slow inference speeds, and limited applicability in real-time applications. In this paper, we propose a novel light-weight Crack Regional Segmentation method based on YOLOv11, which introduces attention mechanisms to address challenges in pavement images, such as varying crack sizes, occlusion, and irregular surface textures. By embedding a region-based attention mechanism into the YOLOv11 network, the method enhances the model’s ability to focus on crack features. Specifically, the model network layers are progressively pruned to reduce the number of parameters and floating-point operations, thereby further improving operational efficiency and refining detection in the target regions. Furthermore, to tackle issues with blurred or indistinct crack boundaries, we present a fuzzy information-guided YOLOv11-based model, FIG-YOLO. This model integrates fuzzy logic and fuzzy membership functions to handle uncertainty in crack detection. The fuzzy membership functions are used to quantify the degree of crack features, allowing the model to better distinguish between crack and non-crack regions, especially in cases where crack boundaries are unclear. This approach significantly improves the accuracy of crack detection and segmentation. Extensive experiments demonstrate that our approach effectively addresses challenges such as complex backgrounds and blurred crack edges in pavement images. This research not only provides a novel solution for the automated detection of pavement cracks but also offers insights into the development of intelligent road maintenance systems. With the expansion of large-scale datasets and the advancement of deep learning models, pavement crack detection algorithms are expected to further enhance their accuracy and efficiency, offering significant support for road infrastructure management.

As a critical component of electric vehicles, the powertrain has a significant impact on the overall performance of vehicles. In addressing the challenge of lengthy testing cycles, this study develops a para model of the powertrain, utilizing digital twin (DT) technology, thereby establishing a framework for simulation testing of multi-controller intermodulation. We establish functional definition coverage testing by designing specific functional requirement use cases, and we validate the failure mechanism via fault injection use cases. The results indicate that the DT testing platform can effectively simulate the operational interactions among various controllers within the powertrain system. In comparison to traditional field testing, the digital twin-based testing methodology offers enhanced operational efficiency and allows for the examination of testing conditions that are impractical to implement in real vehicles, particularly in the context of fault injection testing, thus facilitating the early detection of potential safety risks within the system. The advancement of this technical solution holds significant practical implications for the future mass production and development of electric vehicles.

Controller Area Network with Flexible Data-rate (CAN-FD) is a promising industrial embedded network because of its high bandwidth and long data field length. However, CAN-FD does not deploy any security protection mechanisms, leaving it vulnerable to network attacks. In recent years, authentication and authorization frameworks have often been deployed in industrial embedded networks (e.g., automotive networks) to provide secure CAN/CAN-FD communication. However, these frameworks cannot simultaneously enhance confidentiality, integrity, and availability; moreover, these frameworks are mainly based on a distributed security management mechanism, resulting in large computation, communication, and memory overhead. This paper proposes a trinity-enabled and lightweight security framework called TrinitySec based on cryptographic algorithms for CAN-FD communication. TrinitySec ensures the availability of ECU and CAN-FD messages through authentication and authorization, as well as the confidentiality and integrity of CAN-FD messages through a symmetric-key algorithm and Hash-based Message Authentication Code (HMAC) function. TrinitySec proposes a low-overhead centralized security management mechanism instead of the existing distributed management mechanism. We formally verify the security of TrinitySec using the ProVerif tool. We implement TrinitySec on STM32H743IIT Micro Controller Unit (MCU) with ARM Cortex M7 core and evaluate that TrinitySec outperforms other state-of-the-art security frameworks in terms of computation, communication, memory, and storage overhead.

Accurate bus travel time prediction is crucial for improving travel experience, especially in transfer journeys. This study introduces a novel multi-route bus travel time prediction system-based PMLNet, a partition and combination prediction framework, addressing the gap in accurate prediction models by incorporating macro and local impact factors. The system employs a pre-processing algorithm for constructing travel chains, partitions travel time into four components, utilizes LSTM along with the newly proposed MDARNN model for predicting each component, and applies four real-time traffic impact factors to calibrate the predictions of each component. Experimental validation on four bus routes demonstrates PMLNet’s superior performance, achieving mean absolute percentage errors (MAPE) as low as 2.91% and mean absolute errors (MAE) below 1.45 min, outperforming traditional models and various partitioned combination frameworks. These findings underscore PMLNet’s potential to significantly improve public transportation services by providing more accurate travel time predictions, ultimately enhancing the user experience in intelligent transportation systems.