Lane change prediction, encompassing both intention recognition and trajectory forecasting, is essential for the safe operation of autonomous vehicles in mixed-traffic environments. Existing models predominantly follow a data-driven paradigm, learning directly from historical vehicle states through an end-to-end approach. Inspired by the emerging paradigm of enhancing model generalizability through domain knowledge, we propose KnowLCP to explicitly model and integrate driving knowledge into the lane change prediction task. Specifically, we incorporate three types of knowledge: traffic risk awareness to improve intention prediction, vehicle kinematics to ensure the physical feasibility of predicted trajectories, and intention intensity to refine trajectory forecasting. Furthermore, we introduce a novel knowledge injection strategy that enhances mutual information during integration and proves superior to the traditional parallel input mechanism, which simply feeds knowledge features alongside historical states. Extensive experiments on two real-world trajectory datasets demonstrate that KnowLCP achieves average improvements of 8.3-10.3% in intention prediction and 10.1-10.3% in trajectory prediction over the best-performing baselines.

With the advancement of Industry 5.0 and smart manufacturing, communication networks in smart factories must be efficient, reliable, and energy-saving. However, the dynamic and complex nature of heterogeneous networks poses challenges to traditional energy efficiency optimization methods. To address this, a load prediction model leveraging long short-term memory networks with attention mechanisms (LAM) is developed, along with load prediction and task demand-based strategy to enhance network energy efficiency. Experimental results demonstrate that the proposed method achieves higher network sleep rates and improved energy-saving performance compared to existing solutions, supporting sustainable and efficient smart factory operations.

With the advancement of humanoid robot industrialization, dexterous grasping has become a critical research area. Traditional two-finger grippers, while effective for regular geometries, struggle with complex shapes. Multi-finger dexterous hands offer significant advantages for adapting to diverse objects. This study proposes a grasp pose estimation method for multi-finger dexterous hands utilizing a Conditional Variational Autoencoder (CVAE) framework. Point cloud data and grasp poses from industrial components are used as inputs to the CVAE, with K-Nearest Neighbors (KNN) employed to enhance local feature extraction. Experimental results show that the proposed method achieves robust generalization and stability across various grasping scenarios.

In mixed-autonomy traffic environments, accurately predicting the lane change behavior of human-driven vehicles is critical for ensuring the safety and reliability of autonomous vehicle decision-making. However, existing approaches face two major challenges: 1) they tend to represent the relationships between the target vehicle and surrounding vehicles using parameters like relative position and speed. This approach either requires a fixed number of surrounding vehicles or introduces significant noise by relying on virtual vehicles; and 2) they often fail to fully exploit the vast amount of available vehicle trajectory data, leaving the complexities of vehicular interactions underexplored. To address these issues, this paper presents a novel lane change prediction framework using Transformer-based transfer learning. Our design aims to leverage inter-vehicle interactions learned from trajectory data to improve lane-change prediction accuracy. Specifically, pre-trained trajectory prediction models are used to adapt dynamically to the varying number of surrounding vehicles and to capture interaction context from large sets of trajectory data. We then refine the Transformer model to integrate this context and predict the target vehicle’s lane change intentions. The Transformer encoder transforms trajectory interaction context into a lane-change-oriented context using aggregated multi-head attention. The Transformer decoder, in turn, utilizes this context alongside the target vehicle’s states through relation-aware multi-head attention to forecast lane change behavior. Extensive experiments on two real-world datasets demonstrate that our proposed framework outperforms state-of-the-art baselines in both accuracy and robustness.

The accurate trajectory prediction of surrounding vehicles is crucial for the sustainability and safety of connected and autonomous vehicles under mixed traffic streams in the real world. The task of trajectory prediction is challenging because there are all kinds of factors affecting the motions of vehicles, such as the individual movements, the ambient driving environment especially road conditions, and the interactions with neighboring vehicles. To resolve the above issues, this work proposes a novel Heterogeneous Context-Aware Graph Convolutional Networks following the Encoder-Decoder architecture, which simultaneously extracts the hidden contexts from individual historical trajectories, varying driving scene, and inter-vehicle interactional behaviors. Specifically, the historical vehicle trajectories are fed into Temporal Convolutional Network to capture the individual context. Besides, a 2-Dimensional Convolutional Network with temporal attention is designed for transforming the scene image stream into compressing scene context. Then a Spatio-Temporal Dynamic Graph Convolutional Networks is devised to model the evolving interactional patterns, which incorporates the acquired individual and scene contexts as the representation of the node. Finally, the aforementioned three contexts are combined and fed into the decoder to produce future trajectories. The proposed model is validated on two real-world datasets which contain various driving scenarios. Results demonstrated that the proposed model outperforms state-of-the-art methods in prediction accuracy and achieves immense stability towards different vehicle states.

The routing algorithm based on a single routing metric parameter is difficult to meet the Quality of Services (QoS) requirements of multi-source data flows in the software-defined factory heterogeneous network, resulting in link congestion and waste of network resources. To solve the problem, this paper proposes a QoS optimization method for software-defined factory heterogeneous networks based on the double deep Q network (DDQN). First, a QoS optimization architecture is proposed for software-defined factory heterogeneous networks, and the optimization function for network latency and load balancing is established. Then, the DDQN algorithm is used to obtain the optimal paths of multi-source data flows, and the optimal path is uniformly issued by the software-defined network controller. Experiments showed that the proposed algorithm has good convergence and generalizability. Compared with the existing algorithms, it outperforms in network latency, network jitter, and network throughput, improving network load balancing and routing efficiency. This research is able to provide a solution to the QoS optimization and dynamic traffic scheduling in smart heterogeneous networks and provide a technical reference point for realizing customized smart factories.