A Crowd Model Evaluation Method for Autonomous Mobile Robot Simulator

Crowd models are utilized in various fields, including research on autonomous mobile robots, where they serve as an experimental environment for selecting and evaluating appropriate navigation methods. It is important to note that while there are numerous crowd models available, each has its own strengths and weaknesses, and no single model is universally applicable to all scenarios. Therefore, selecting the appropriate model for a crowded environment can be a challenging task that requires careful consideration and evaluation. Against this background, we have studied methods to classify scenarios for simulations into multiple classes and select appropriate crowd models based on the classification results. In this paper, a method is proposed for evaluating various crowd models in this context. The paper presents a method for comparing pedestrian movement trajectory data from a dataset with the results of a crowd simulation in an equivalent environment. The comparison is carried out using the visual shape of the movement trajectories and Dynamic Time Warping. The effectiveness of the method is validated by evaluating the impact of the crowd model and its parameter values on the accuracy of simulating a particular scenario.