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An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces

Manon Prédhumeau 1, 2 Lyuba Mancheva 1, 2 Julie Dugdale 1, 2 Anne Spalanzani 3, 2
1 HAwAI - Human Aware Artificial Intelligence
LIG - Laboratoire d'Informatique de Grenoble
3 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
Abstract : This paper addresses modeling and simulating pedestrian trajectories when interacting with an autonomous vehicle in a shared space. Pedestrian motion models integrating pedestrians interactions with an autonomous vehicle already exist. However, they fail to accurately predict the individual trajectory of each pedestrian, and they do not deal with the diversity of possible pedestrian interactions with the vehicle in a shared space (front, back or lateral). Moreover, previous works do not sufficiently provide a quantitative evaluation of the model's predictions. In this paper, we propose an hybrid pedestrian model that combines the social force model and a new decision model for conflicting pedestrian-vehicle interactions. The proposed model integrates different observed pedestrians behaviors, as well as the behaviors of the social groups of pedestrians. We validate the model and evaluate its predictive potential through qualitative and quantitative comparisons with ground truth trajectories. The proposed model reproduces observed behaviors that have not been replicated by the social force model and outperforms the social force model at predicting pedestrians trajectories on the used dataset. This model will be used by an autonomous vehicle in a shared space to predict the trajectories of surrounding pedestrians.
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https://hal.archives-ouvertes.fr/hal-03113422
Contributor : Manon Prédhumeau <>
Submitted on : Wednesday, January 20, 2021 - 2:03:20 PM
Last modification on : Thursday, March 25, 2021 - 1:55:47 PM

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Manon Prédhumeau, Lyuba Mancheva, Julie Dugdale, Anne Spalanzani. An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces. AAMAS 2021 - 20th International Conference on Autonomous Agents and Multiagent Systems, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), May 2021, Online, France. pp.1-9. ⟨hal-03113422v2⟩

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