Generating Human-Like Movement: A Comparison Between Two Approaches Based on Environmental Features

TL;DR

This paper compares two algorithms for generating human-like movement trajectories based on environmental features, evaluating their realism, efficiency, and sensitivity to parameters.

Contribution

It introduces and compares Attraction-Based A* and Feature-Based A* algorithms that incorporate environmental features and real trajectories for realistic movement modeling.

Findings

Feature-Based A* produces more realistic trajectories.

Attraction-Based A* is more time-efficient.

Feature-Based A* is sensitive to hyper-parameters.

Abstract

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.