[2511.20008] Pedestrian Crossing Intention Prediction Using Multimodal Fusion Network

Computer Science > Computer Vision and Pattern Recognition arXiv:2511.20008 (cs) [Submitted on 25 Nov 2025 (v1), last revised 24 Mar 2026 (this version, v2)] Title:Pedestrian Crossing Intention Prediction Using Multimodal Fusion Network Authors:Yuanzhe Li, Steffen Müller View a PDF of the paper titled Pedestrian Crossing Intention Prediction Using Multimodal Fusion Network, by Yuanzhe Li and Steffen M\"uller View PDF Abstract:Pedestrian crossing intention prediction is essential for the deployment of autonomous vehicles (AVs) in urban environments. Ideal prediction provides AVs with critical environmental cues, thereby reducing the risk of pedestrian-related collisions. However, the prediction task is challenging due to the diverse nature of pedestrian behavior and its dependence on multiple contextual factors. This paper proposes a multimodal fusion network that leverages seven modality features from both visual and motion branches, aiming to effectively extract and integrate complementary cues across different modalities. Specifically, motion and visual features are extracted from the raw inputs using multiple Transformer-based extraction modules. Depth-guided attention module leverages depth information to guide attention towards salient regions in another modality through comprehensive spatial feature interactions. To account for the varying importance of different modalities and frames, modality attention and temporal attention are designed to selectively emphasize in...