Generating Human-Like Movement: A Comparison Between Two Approaches Based on Environmental Features
This research addresses the problem of generating realistic human movement in simulations for applications like leisure trip planning, which currently do not adequately consider environmental features.
This paper compares two novel algorithms, Attraction-Based A* and Feature-Based A*, for generating human-like trajectories based on environmental features. While Feature-Based A* generated trajectories closer to real ones, it was less time-efficient than Attraction-Based A*, limiting its real-world usability.
Modelling realistic human behaviours in simulation is an ongoing challenge that resides between several fields like social sciences, philosophy, and artificial intelligence. Human movement is a special type of behaviour driven by intent (e.g. to get groceries) and the surrounding environment (e.g. curiosity to see new interesting places). Services available online and offline do not normally consider the environment when planning a path, which is decisive especially on a leisure trip. Two novel algorithms have been presented to generate human-like trajectories based on environmental features. The Attraction-Based A* algorithm includes in its computation information from the environmental features meanwhile, the Feature-Based A* algorithm also injects information from the real trajectories in its computation. The human-likeness aspect has been tested by a human expert judging the final generated trajectories as realistic. This paper presents a comparison between the two approaches in some key metrics like efficiency, efficacy, and hyper-parameters sensitivity. We show how, despite generating trajectories that are closer to the real one according to our predefined metrics, the Feature-Based A* algorithm fall short in time efficiency compared to the Attraction-Based A* algorithm, hindering the usability of the model in the real world.