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” is published in two issues (Vol.37 No.5 and No.6). The present issue corresponds to No.5 and features 20 selected papers. The accepted papers are categorized as follows: • Reinforcement learning and machine learning • Control theory and algorithms • Sensing, simulation, and system identification • Human–machine interface The papers in this issue are organized to present the most recent advances in reinforcement learning and machine learning for mobility systems, followed by significant contributions to control theory and algorithms. The subsequent sections introduce the progress in sensing, simulation, and system identification technologies, and the issue concludes with studies focusing on human–machine interfaces. These contributions encompass diverse application domains, including automobiles, ships, mobile robots, and heavy-duty vehicles, which are systematically arranged according to their technical approaches. We would like to express our gratitude to all the authors and reviewers and hope that this special issue will contribute to future research and development in vehicle and mobile robot technology.
Minami et al. (Sun,) studied this question.