Generation of Human-Like Movements Based on Environmental Features


Modelling human behaviour in simulation is still an ongoing challenge that spaces between several fields like social science, artificial intelligence, and philosophy. Humans normally move driven by their intent (e.g. to get groceries) and the surrounding environment (e.g. curiosity to see new interesting places). Normal services available online and offline do not consider the environment when planning the path. Especially on a leisure trip, this is very important. This paper presents a comparison between different machine learning algorithms and a famous path planning algorithm in the task of generating human- like trajectories based on environmental features. We show how a modified version of the well known A* algorithm outperforms different machine learning algorithms by computed evaluation metrics and human evaluation in the task of generating bike trips in the area around Ljubljana, Slovenia.

2019 IEEE Symposium Series on Computational Intelligence (SSCI).