Towards Explainable Pedestrian Behavior Prediction: A Neuro-Symbolic Framework for Autonomous Driving
In the context of autonomous driving, predicting pedestrian behavior is a critical component for enhancing road safety. Currently, the focus of such predictions extends beyond accuracy and reliability, placing increasing emphasis on the explainability and interpretability of the models. This researc...
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| Language: | English |
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/6283 |
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| author | Angie Nataly Melo Castillo Carlota Salinas Maldonado Miguel Ángel Sotelo |
| author_facet | Angie Nataly Melo Castillo Carlota Salinas Maldonado Miguel Ángel Sotelo |
| author_sort | Angie Nataly Melo Castillo |
| collection | DOAJ |
| description | In the context of autonomous driving, predicting pedestrian behavior is a critical component for enhancing road safety. Currently, the focus of such predictions extends beyond accuracy and reliability, placing increasing emphasis on the explainability and interpretability of the models. This research presents a novel neuro-symbolic approach that integrates deep learning with fuzzy logic to develop a pedestrian behavior predictor. The proposed model leverages a set of explainable features and utilizes a fuzzy inference system to determine whether a pedestrian is likely to cross the street. The pipeline was trained and evaluated using both the Pedestrian Intention Estimation (PIE) and Joint Attention for Autonomous Driving (JAAD) datasets. The results provide experimental insights into achieving greater explainability in pedestrian behavior prediction. Additionally, the proposed method was applied to assess the data selection process through a series of experiments, leading to a set of guidelines and recommendations for data selection, feature engineering, and explainability. |
| format | Article |
| id | doaj-art-fa9e17394db84b4281b9e57df48c926f |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-fa9e17394db84b4281b9e57df48c926f2025-08-20T03:46:52ZengMDPI AGApplied Sciences2076-34172025-06-011511628310.3390/app15116283Towards Explainable Pedestrian Behavior Prediction: A Neuro-Symbolic Framework for Autonomous DrivingAngie Nataly Melo Castillo0Carlota Salinas Maldonado1Miguel Ángel Sotelo2Computer Engineering Department, University of Alcalá, Ctra. Madrid-Barcelona km. 33, 28805 Alcalá de Henares, SpainComputer Engineering Department, University of Alcalá, Ctra. Madrid-Barcelona km. 33, 28805 Alcalá de Henares, SpainComputer Engineering Department, University of Alcalá, Ctra. Madrid-Barcelona km. 33, 28805 Alcalá de Henares, SpainIn the context of autonomous driving, predicting pedestrian behavior is a critical component for enhancing road safety. Currently, the focus of such predictions extends beyond accuracy and reliability, placing increasing emphasis on the explainability and interpretability of the models. This research presents a novel neuro-symbolic approach that integrates deep learning with fuzzy logic to develop a pedestrian behavior predictor. The proposed model leverages a set of explainable features and utilizes a fuzzy inference system to determine whether a pedestrian is likely to cross the street. The pipeline was trained and evaluated using both the Pedestrian Intention Estimation (PIE) and Joint Attention for Autonomous Driving (JAAD) datasets. The results provide experimental insights into achieving greater explainability in pedestrian behavior prediction. Additionally, the proposed method was applied to assess the data selection process through a series of experiments, leading to a set of guidelines and recommendations for data selection, feature engineering, and explainability.https://www.mdpi.com/2076-3417/15/11/6283autonomous drivingexplainabilityinterpretabilitypedestrian behavior predictionneuro-symbolicdataset selection |
| spellingShingle | Angie Nataly Melo Castillo Carlota Salinas Maldonado Miguel Ángel Sotelo Towards Explainable Pedestrian Behavior Prediction: A Neuro-Symbolic Framework for Autonomous Driving Applied Sciences autonomous driving explainability interpretability pedestrian behavior prediction neuro-symbolic dataset selection |
| title | Towards Explainable Pedestrian Behavior Prediction: A Neuro-Symbolic Framework for Autonomous Driving |
| title_full | Towards Explainable Pedestrian Behavior Prediction: A Neuro-Symbolic Framework for Autonomous Driving |
| title_fullStr | Towards Explainable Pedestrian Behavior Prediction: A Neuro-Symbolic Framework for Autonomous Driving |
| title_full_unstemmed | Towards Explainable Pedestrian Behavior Prediction: A Neuro-Symbolic Framework for Autonomous Driving |
| title_short | Towards Explainable Pedestrian Behavior Prediction: A Neuro-Symbolic Framework for Autonomous Driving |
| title_sort | towards explainable pedestrian behavior prediction a neuro symbolic framework for autonomous driving |
| topic | autonomous driving explainability interpretability pedestrian behavior prediction neuro-symbolic dataset selection |
| url | https://www.mdpi.com/2076-3417/15/11/6283 |
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