Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention

TL;DR

This paper introduces a novel neural network architecture that fuses various spatio-temporal features using attention mechanisms to improve real-time pedestrian crossing intention prediction, achieving state-of-the-art results on the JAAD benchmark.

Contribution

The work presents a new neural network model that effectively combines RGB sequences, semantic masks, and vehicle speed with attention and RNNs for better pedestrian intention prediction.

Findings

Achieved state-of-the-art performance on JAAD benchmark.

Demonstrated the effectiveness of feature fusion with attention mechanisms.

Validated the architecture through extensive ablation studies.

Abstract

Predicting vulnerable road user behavior is an essential prerequisite for deploying Automated Driving Systems (ADS) in the real-world. Pedestrian crossing intention should be recognized in real-time, especially for urban driving. Recent works have shown the potential of using vision-based deep neural network models for this task. However, these models are not robust and certain issues still need to be resolved. First, the global spatio-temproal context that accounts for the interaction between the target pedestrian and the scene has not been properly utilized. Second, the optimum strategy for fusing different sensor data has not been thoroughly investigated. This work addresses the above limitations by introducing a novel neural network architecture to fuse inherently different spatio-temporal features for pedestrian crossing intention prediction. We fuse different phenomena such as sequences of RGB imagery, semantic segmentation masks, and ego-vehicle speed in an optimum way using attention mechanisms and a stack of recurrent neural networks. The optimum architecture was obtained through exhaustive ablation and comparison studies. Extensive comparative experiments on the JAAD pedestrian action prediction benchmark demonstrate the effectiveness of the proposed method, where state-of-the-art performance was achieved. Our code is open-source and publicly available.