# RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features

arXiv: https://arxiv.org/abs/2008.11647 · HTML: https://mlsanity.com/paper/2008.11647/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 21 of 21)

- [Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention](https://mlsanity.com/paper/2104.05485/) (2021)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture](https://mlsanity.com/paper/2105.08647/) (2021)
- [PIP-Net: Pedestrian Intention Prediction in the Wild](https://mlsanity.com/paper/2402.12810/) (2024)
- [Deep Virtual-to-Real Distillation for Pedestrian Crossing Prediction](https://mlsanity.com/paper/2211.00856/) (2022)
- [Testing predictive automated driving systems: lessons learned and future recommendations](https://mlsanity.com/paper/2205.10115/) (2022)
- [On-Board Pedestrian Trajectory Prediction Using Behavioral Features](https://mlsanity.com/paper/2210.11999/) (2022)
- [WatchPed: Pedestrian Crossing Intention Prediction Using Embedded Sensors of Smartwatch](https://mlsanity.com/paper/2208.07441/) (2022)
- [Context-aware Multi-task Learning for Pedestrian Intent and Trajectory Prediction](https://mlsanity.com/paper/2407.17162/) (2024)
- [SGNetPose+: Stepwise Goal-Driven Networks with Pose Information for Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2503.08016/) (2025)
- [TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations](https://mlsanity.com/paper/2508.19866/) (2025)
- [Single Shot Multitask Pedestrian Detection and Behavior Prediction](https://mlsanity.com/paper/2101.02232/) (2021)
- [Feature Importance in Pedestrian Intention Prediction: A Context-Aware Review](https://mlsanity.com/paper/2409.07645/) (2024)
- [Pedestrian Intention and Trajectory Prediction in Unstructured Traffic Using IDD-PeD](https://mlsanity.com/paper/2506.22111/) (2025)
- [Occlusion-Aware Diffusion Model for Pedestrian Intention Prediction](https://mlsanity.com/paper/2511.00858/) (2025)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)
- [Temporal-contextual Event Learning for Pedestrian Crossing Intent Prediction](https://mlsanity.com/paper/2504.06292/) (2025)
- [Context-free Self-Conditioned GAN for Trajectory Forecasting](https://mlsanity.com/paper/2603.08658/) (2026)
- [ARCANE-PedSynth: Synthetic Multi-Pedestrian Datasets with Behavioural Crossing Annotations](https://mlsanity.com/paper/2605.24950/) (2026)
- [Unified Vision-Centric Pedestrian Crossing Action Prediction via Adaptive Patch Projection and Proactive Spatial Rectification](https://mlsanity.com/paper/2609.07420/) (2026)
- [MTF-Net: Multi-Modal Temporal Feature Fusion Network for Pedestrian Intention Prediction](https://mlsanity.com/paper/2609.20178/) (2026)

## Abstract (arXiv)

Pedestrian crossing prediction is a crucial task for autonomous driving. Numerous studies show that an early estimation of the pedestrian's intention can decrease or even avoid a high percentage of accidents. In this paper, different variations of a deep learning system are proposed to attempt to solve this problem. The proposed models are composed of two parts: a CNN-based feature extractor and an RNN module. All the models were trained and tested on the JAAD dataset. The results obtained indicate that the choice of the features extraction method, the inclusion of additional variables such as pedestrian gaze direction and discrete orientation, and the chosen RNN type have a significant impact on the final performance.