# Experimental Insights Towards Explainable and Interpretable Pedestrian Crossing Prediction

arXiv: https://arxiv.org/abs/2312.02872 · HTML: https://mlsanity.com/paper/2312.02872/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 13 of 13)

- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Selection via Proxy: Efficient Data Selection for Deep Learning](https://mlsanity.com/paper/1906.11829/) (2019)
- [Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction](https://mlsanity.com/paper/2002.08945/) (2020)
- [TITAN: Future Forecast using Action Priors](https://mlsanity.com/paper/2003.13886/) (2020)
- [Interpretability and Explainability: A Machine Learning Zoo Mini-tour](https://mlsanity.com/paper/2012.01805/) (2020)
- [Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs](https://mlsanity.com/paper/2005.06582/) (2020)
- [YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception](https://mlsanity.com/paper/2208.11434/) (2022)
- [DataHub: Collaborative Data Science & Dataset Version Management at Scale](https://mlsanity.com/paper/1409.0798/) (2014)
- [Testing predictive automated driving systems: lessons learned and future recommendations](https://mlsanity.com/paper/2205.10115/) (2022)
- [PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation](https://mlsanity.com/paper/2204.11548/) (2022)
- [Analysis over vision-based models for pedestrian action anticipation](https://mlsanity.com/paper/2305.17451/) (2023)

## Built on by (most cited first, 1 of 1)

- [RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2405.00449/) (2024)

## Abstract (arXiv)

In the context of autonomous driving, pedestrian crossing prediction is a key component for improving road safety. Presently, the focus of these predictions extends beyond achieving trustworthy results; it is shifting towards the explainability and interpretability of these predictions. This research introduces a novel neuro-symbolic approach that combines deep learning and fuzzy logic for an explainable and interpretable pedestrian crossing prediction. We have developed an explainable predictor (ExPedCross), which utilizes a set of explainable features and employs a fuzzy inference system to predict whether the pedestrian will cross or not. Our approach was evaluated on both the PIE and JAAD datasets. The results offer experimental insights into achieving explainability and interpretability in the pedestrian crossing prediction task. Furthermore, the testing results yield a set of guidelines and recommendations regarding the process of dataset selection, feature selection, and explainability.