南 裕樹

Recently, mobility technologies have undergone remarkable advances, with innovations such as autonomous driving and vehicle electrification, becoming increasingly integrated into society. The deployment of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications has accelerated the realization of connected cars, and the use of advanced driver assistance systems (ADAS) powered by artificial intelligence (AI) and machine learning continues to expand. Simultaneously, the emergence of mobility as a service (MaaS) and the introduction of smart infrastructure are becoming essential to ensure safe and efficient urban transportation. Collectively, these developments have reshaped the concept of mobility and driven the global transition toward autonomous driving. This special issue on “Vehicle and Mobile Robot Technology” consists of two issues (Vol.37 No.5 and No.6). The preceding issue (Vol.37 No.5) presented 20 selected papers organized into four technical categories: reinforcement learning and machine learning; control theory and algorithms; sensing, simulation, and system identification; and human–machine interfaces. The present issue (Vol.37 No.6) further extends this exploration by presenting 19 papers that reflect the growing diversity and sophistication of research in vehicle and mobile robot systems. The contributions in this issue are organized into five thematic categories: • Navigation and path planning • Localization, SLAM, and environmental perception • Control systems • Cooperative control and multi-robot systems • Human behavior modeling and traffic dynamics These categories capture the central areas of emphasis in contemporary mobility research, encompassing advances in autonomous navigation and scene understanding, improvements in sensing and localization under real-world conditions, progress in vehicle and robot control methodologies, the growth of multi-agent and swarm-based approaches, and the integration of human behavior models into transportation and mobility systems. Together, these works illustrate the ongoing evolution of technologies that support safe, intelligent, and adaptive mobility. We would like to express our sincere appreciation to all the authors for their valuable contributions, as well as the reviewers for their diligent efforts in ensuring the quality of this issue. We hope that this special issue will stimulate further research and development and offer new insights into the future of vehicle and mobile robot technology.

One of the main challenges in enhancing vehicle safety is mitigating the effects of disturbances such as crosswinds. This study focused on compensating for crosswind disturbances using vehicle-to-infrastructure (V2I) communication, which allows vehicles to receive information from the roads. In this paper, we first developed a disturbance compensation method based on a traditional observer and a technique known as prediction governor. The prediction governor shapes the disturbance estimates by leveraging actual past information obtained from V2I communication. Then, we validated the effectiveness of the proposed method using several numerical simulations.