# Assessing Cross-dataset Generalization of Pedestrian Crossing Predictors

arXiv: https://arxiv.org/abs/2201.12626 · HTML: https://mlsanity.com/paper/2201.12626/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 49 references that are in the map.
- First posted on arXiv 29 Jan 2022.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Are we done with ImageNet?](https://mlsanity.com/paper/2006.07159/) (2020)
- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches](https://mlsanity.com/paper/1803.04386/) (2018)
- [Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty](https://mlsanity.com/paper/1711.09026/) (2017)
- [Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs](https://mlsanity.com/paper/2005.06582/) (2020)
- [Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention](https://mlsanity.com/paper/2104.05485/) (2021)
- [Real-time Intent Prediction of Pedestrians for Autonomous Ground Vehicles via Spatio-Temporal DenseNet](https://mlsanity.com/paper/1904.09862/) (2019)
- [Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction](https://mlsanity.com/paper/1910.09077/) (2019)
- [Uncertainty-Aware Attention for Reliable Interpretation and Prediction](https://mlsanity.com/paper/1805.09653/) (2018)
- [Is attention to bounding boxes all you need for pedestrian action prediction?](https://mlsanity.com/paper/2107.08031/) (2021)
- [CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems](https://mlsanity.com/paper/2010.05139/) (2020)
- [IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture](https://mlsanity.com/paper/2105.08647/) (2021)
- [TrouSPI-Net: Spatio-temporal attention on parallel atrous convolutions and U-GRUs for skeletal pedestrian crossing prediction](https://mlsanity.com/paper/2109.00953/) (2021)
- [Action and intention recognition of pedestrians in urban traffic](https://mlsanity.com/paper/1810.09805/) (2018)
- [VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users](https://mlsanity.com/paper/2007.05397/) (2020)
- [Asymmetrical Bi-RNN for pedestrian trajectory encoding](https://mlsanity.com/paper/2106.04419/) (2021)

## Built on by (most cited first, 11 of 11)

- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Uncertainty estimation for Cross-dataset performance in Trajectory prediction](https://mlsanity.com/paper/2205.07310/) (2022)
- [PIP-Net: Pedestrian Intention Prediction in the Wild](https://mlsanity.com/paper/2402.12810/) (2024)
- [Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2405.19202/) (2024)
- [How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?](https://mlsanity.com/paper/2301.04414/) (2023)
- [Local and Global Contextual Features Fusion for Pedestrian Intention Prediction](https://mlsanity.com/paper/2305.01111/) (2023)
- [Diving Deeper Into Pedestrian Behavior Understanding: Intention Estimation, Action Prediction, and Event Risk Assessment](https://mlsanity.com/paper/2407.00446/) (2024)
- [Continual Cross-Dataset Adaptation in Road Surface Classification](https://mlsanity.com/paper/2309.02210/) (2023)
- [BEVal: A Cross-dataset Evaluation Study of BEV Segmentation Models for Autonomous Driving](https://mlsanity.com/paper/2408.16322/) (2024)
- [Learning from the Unseen: Generative Data Augmentation for Geometric-Semantic Accident Anticipation](https://mlsanity.com/paper/2605.00051/) (2026)
- [TrajFusionNet+: Transformer-Based Prediction of Pedestrian Crossing Intention via Fusion of Trajectory Representations and Scene Graphs](https://mlsanity.com/paper/2609.10806/) (2026)

## Abstract (arXiv)

Pedestrian crossing prediction has been a topic of active research, resulting in many new algorithmic solutions. While measuring the overall progress of those solutions over time tends to be more and more established due to the new publicly available benchmark and standardized evaluation procedures, knowing how well existing predictors react to unseen data remains an unanswered question. This evaluation is imperative as serviceable crossing behavior predictors should be set to work in various scenarii without compromising pedestrian safety due to misprediction. To this end, we conduct a study based on direct cross-dataset evaluation. Our experiments show that current state-of-the-art pedestrian behavior predictors generalize poorly in cross-dataset evaluation scenarii, regardless of their robustness during a direct training-test set evaluation setting. In the light of what we observe, we argue that the future of pedestrian crossing prediction, e.g. reliable and generalizable implementations, should not be about tailoring models, trained with very little available data, and tested in a classical train-test scenario with the will to infer anything about their behavior in real life. It should be about evaluating models in a cross-dataset setting while considering their uncertainty estimates under domain shift.