A context-aware pedestrian trajectory prediction framework for automated vehicles

TL;DR

This paper introduces a context-aware pedestrian trajectory prediction framework for automated vehicles using a novel LSTM-based model that incorporates environmental and behavioral data, validated on VR and real datasets, enhancing prediction accuracy.

Contribution

It presents a new multi-input LSTM model that integrates pedestrian head orientation and vehicle distance, improving trajectory prediction in automated vehicle scenarios.

Findings

Incorporating contextual information reduces prediction error.

The framework performs well on both VR and real datasets.

Model interpretability offers insights for AV sensing system improvements.

Abstract

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.