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Communication Dans Un Congrès Année : 2023

Analysis over vision-based models for pedestrian action anticipation

Résumé

Anticipating human actions in front of autonomous vehicles is a challenging task. Several papers have recently proposed model architectures to address this problem by combining multiple input features to predict pedestrian crossing actions. This paper focuses specifically on using images of the pedestrian's context as an input feature. We present several spatio-temporal model architectures that utilize standard CNN and Transformer modules to serve as a backbone for pedestrian anticipation. However, the objective of this paper is not to surpass state-of-the-art benchmarks but rather to analyze the positive and negative predictions of these models. Therefore, we provide insights on the explainability of vision-based Transformer models in the context of pedestrian action prediction. We will highlight cases where the model can achieve correct quantitative results but falls short in providing human-like explanations qualitatively, emphasizing the importance of investing in explainability for pedestrian action anticipation problems.

Dates et versions

hal-04370054 , version 1 (02-01-2024)

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Paternité

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Lina Achaji, Julien Moreau, François Aioun, François Charpillet. Analysis over vision-based models for pedestrian action anticipation. 3rd Workshop on the Prediction of Pedestrian Behaviors for Automated Driving @ 26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023, IEEE, Sep 2023, Bilbao, Spain. pp.6. ⟨hal-04370054⟩
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