Automatic Modulation Recognition (AMR) is becoming increasingly important due to its key role in wireless communications. In order to enrich the feature information and reduce computational complexity, a novel feature fusion convolution-aided transformer (FCAformer) approach is proposed in this paper. To enhance the differences between samples, we adopt Markov Transformation Field (MTF), which can extract potential correlation features between signal sequences and combine them with I/Q sequences to generate more information rich data as input for the transformer. We propose a novel patchify module that reduces invalid features and computational complexity by performing feature extraction. We also propose a convolution-aided encoder module consisting of an improved attention mechanism and a convolutional residual connection to improve performance through local-global feature fusion and further reduce the parameters. Experimental results on RadioRML2016.10a show that the proposed AMR method has both higher recognition accuracy and lower parameters than other contrastive methods.

With the rapid increase in vehicle ownership and increasingly stringent emission regulations, addressing the energy consumption of and emissions from commercial vehicles have become critical challenges. This study introduces a multi-objective cooperative adaptive cruise control (CACC) strategy, designed for intelligent connected commercial vehicle platoons, operating in event-triggered conditions. A hierarchical control framework is utilized: the upper layer handles reference speed planning based on vehicle dynamics and constraints, while the lower layer uses distributed model predictive control (DMPC) to manage vehicle following. DMPC is chosen for its ability to manage distributed platoons by enabling vehicles to make local decisions, while maintaining system-wide coordination. Additionally, adaptive particle swarm optimization (APSO) is employed during the optimization process to solve the optimal problem efficiently. APSO is employed for its computational efficiency and adaptability, ensuring quick convergence to optimal solutions with reduced overheads. An event-triggering mechanism is integrated to further reduce the computational demands. The simulation results show that the proposed approach reduces fuel consumption by 8.05% and NO_x emissions by 10.15%, while ensuring stable platoon operation during dynamic driving conditions. The effectiveness of the control strategy is validated through extensive simulations, highlighting superior performance compared to conventional methods.

The Conflict-Based Search (CBS) algorithm is one of the Multi-Agent Path Finding (MAPF) algorithms that has received much attention in recent years. It is a two-level algorithm. The high-level detects and resolves conflicts on a Constraint Tree (CT). Its low-level algorithm is to plan the optimal path for a given agent that satisfies all the constraints of the agent. In this paper, the Space-Time A* (STA*) algorithm is used as the low-level algorithm, which adds the time dimension to the A* algorithm. It means that the planning path for a single agent tends to generate and extend more and more sub-nodes. As the complexity of the problem increases, the efficiency of the low-level solution becomes an increasingly large part of the overall solution efficiency. This leads to an inefficient overall solution. To address this problem, an improved CBS algorithm is proposed. This algorithm is used when the current node in CT has conflicts. It uses current node information and Multi-value Decision Diagram (MDD) technology to pre-plan before formal re-planning. The pre-planning phase entails obtaining information pertaining to the path that satisfies the new constraint, which is referred to as pre-planning information. During the formal re-planning, the pre-planning information is used to guide the low-level algorithm to select the generated sub-nodes. The simulation experiments show that CBS with pre-planning improves the solution time by 24.4% and reduces the search space by 38.7%.