Service Mode Switching for Autonomous Robots and Small Intelligent Vehicles Using Pedestrian Personality Categorization and Flow Series Fluctuation

Autonomous robots and small intelligent vehicles with diverse service functions have been extensively researched and are expected to be deployed in scenarios such as sci-tech parks, museums, and transportation hubs. Although designed as AI-driven assistants, they may not always provide optimal customer service. A key challenge is achieving service intelligence, where adaptive mode switching plays a critical role. Our experimental research demonstrates that the composition of pedestrian types can be inferred from microscopic flow fluctuations. This finding enables the development of effective service mode switching strategies. Therefore, this article proposes a method that classifies pedestrians by their temperament-based behaviors, simulates their movement, and extracts microscopic features from flow data using moving standard deviation (MSTD) and moving root mean square (MRMS) indicators. Analysis of these features enables inference of approximate composition ratio of different pedestrian types, consequently enabling a targeted switching mechanism between active and passive service modes. Simulations confirm that each pedestrian type exhibits distinct flow patterns, and the employed indicators can effectively estimate pedestrian ratios through microscopic flow data analysis, thereby facilitating efficient service mode switching. Furthermore, validation using pedestrian flow data extracted from real-world video footage confirms the method’s applicability and effectiveness.