# A context-aware pedestrian trajectory prediction framework for automated vehicles

arXiv: https://arxiv.org/abs/2104.08123 · HTML: https://mlsanity.com/paper/2104.08123/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2021.

## Built on by (most cited first, 10 of 10)

- [Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios](https://mlsanity.com/paper/2504.01409/) (2025)
- [eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference](https://mlsanity.com/paper/2205.05756/) (2022)
- [Smooth-Trajectron++: Augmenting the Trajectron++ behaviour prediction model with smooth attention](https://mlsanity.com/paper/2305.19678/) (2023)
- [Human trajectory prediction using LSTM with Attention mechanism](https://mlsanity.com/paper/2309.00331/) (2023)
- [Generative Models and Connected and Automated Vehicles: A Survey in Exploring the Intersection of Transportation and AI](https://mlsanity.com/paper/2403.10559/) (2024)
- [Eye Gaze-Informed and Context-Aware Pedestrian Trajectory Prediction in Shared Spaces with Automated Shuttles: A Virtual Reality Study](https://mlsanity.com/paper/2603.19812/) (2026)
- [Development of Human Motion Prediction Strategy using Inception Residual Block](https://mlsanity.com/paper/2108.04001/) (2021)
- [Towards a large-scale fused and labeled dataset of human pose while interacting with robots in shared urban areas](https://mlsanity.com/paper/2402.10077/) (2024)
- [From GEV to ResLogit: Spatially Correlated Discrete Choice Models for Pedestrian Movement Prediction](https://mlsanity.com/paper/2603.01325/) (2026)
- [PoseDriver: A Unified Approach to Multi-Category Skeleton Detection for Autonomous Driving](https://mlsanity.com/paper/2603.23215/) (2026)

## Abstract (arXiv)

With the unprecedented shift towards automated urban environments in recent years, a new paradigm is required to study pedestrian behaviour. Studying pedestrian behaviour in futuristic scenarios requires modern data sources that consider both the Automated Vehicle (AV) and pedestrian perspectives. Current open datasets on AVs predominantly fail to account for the latter, as they do not include an adequate number of events and associated details that involve pedestrian and vehicle interactions. To address this issue, we propose using Virtual Reality (VR) data as a complementary resource to current datasets, which can be designed to measure pedestrian behaviour under specific conditions. In this research, we focus on the context-aware pedestrian trajectory prediction framework for automated vehicles at mid-block unsignalized crossings. For this purpose, we develop a novel multi-input network of Long Short-Term Memory (LSTM) and fully connected dense layers. In addition to past trajectories, the proposed framework incorporates pedestrian head orientations and distance to the upcoming vehicles as sequential input data. By merging the sequential data with contextual information of the environment, we train a model to predict the future pedestrian trajectory. Our results show that the prediction error and overfitting to the training data are reduced by considering contextual information in the model. To analyze the application of the methods to real AV data, the proposed framework is trained and applied to pedestrian trajectory extracted from an open-access video dataset. Finally, by implementing a game theory-based model interpretability method, we provide detailed insights and propose recommendations to improve the current automated vehicle sensing systems from a pedestrian-oriented point of view.