Unsignalized intersections pose a significant challenge for multi-vehicle cooperative decision-making, where safety and efficiency must be ensured simultaneously under dynamic traffic conditions. To address this challenge, this study proposes a Vehicle-to-Infrastructure Integrated Distributed Agent Decision-Making (V2I-IDADM) framework, which leverages vehicle–infrastructure cooperation to enhance both coordination performance and safety at unsignalized intersections. Built upon a learning paradigm with centralized training and decentralized execution, the proposed V2I-IDADM framework utilizes the global perception of roadside infrastructure to assign passing priorities to connected and autonomous vehicles (CAVs), ensuring safe passage through intersections. Meanwhile, the framework’s global coordination mechanism constructs a unified and structured representation of intersection-level traffic states, enabling scalable, real-time decision-making. Specifically, a priority-based safety decision model is developed by jointly integrating passing-priority constraints with action optimization to promote multi-vehicle cooperation. To enhance training efficiency, a hierarchical weighted sampling strategy is introduced to emphasize high-value episodic experiences and accelerate iterative self-learning. Extensive experiments in pure CAV and mixed-traffic scenarios demonstrate that the proposed framework achieves superior safety and efficiency compared with state-of-the-art methods. Experiments conducted on both a miniature intelligent vehicle platform and a full-scale vehicle further validate the practical feasibility and deployment potential of the proposed framework. 

The formation control of unmanned aerial vehicle (UAV) swarms is of significant importance in various fields such as transportation, emergency management, and environmental monitoring. However, the complex dynamics, nonlinearity, uncertainty, and interaction among agents make it a challenging problem. In this paper, we propose a distributed robust control strategy that uses only local information of UAVs to improve the stability and robustness of the formation system in uncertain environments. We establish a nominal control strategy based on position relations and a semi-definite programming model to obtain control gains. Additionally, we propose a robust control strategy under the rotation set $\Omega$ to address the noise and disturbance in the system, ensuring that even when the rotation angles of the UAVs change, they still form a stable formation. Finally, we extend the proposed strategy to a quadrotor UAV system with high-order kinematic models and conduct simulation experiments to validate its effectiveness in resisting uncertain disturbances and achieving formation control.

Self-driving vehicles require a number of tests to prevent fatal accidents and ensure their appropriate operation in the physical world. However, conducting vehicle tests on the road is difficult because such tests are expensive and labor intensive. In this study, we used an autonomous-driving simulator, and investigated the three-dimensional environmental perception problem of the simulated system. Using the open-source CARLA simulator, we generated a CarlaSim from unreal traffic scenarios, comprising 15 000 camera-LiDAR (Light Detection and Ranging) samples with annotations and calibration files. Then, we developed Multi-Sensor Fusion Perception (MSFP) model for consuming two-modal data and detecting objects in the scenes. Furthermore, we conducted experiments on the KITTI and CarlaSim datasets; the results demonstrated the effectiveness of our proposed methods in terms of perception accuracy, inference efficiency, and generalization performance. The results of this study will faciliate the future development of autonomous-driving simulated tests.