EffIntentGCN: An Efficient Graph Convolutional Network for Skeleton-Based Pedestrian Crossing Intention Prediction

Jiaqi Shi, Chaoran Liu, Carlos Toshinori Ishi, Hiroshi Ishiguro

Abstract: Understanding pedestrian behavior and predicting their intentions near roads are crucial for enhancing road safety and traffic efficiency. In intelligent transportation systems, especially where computational and energy resources are limited, real-time inference is vital. To address this, our study focuses on pedestrian skeletal information, which offers lower dimensionality and reduces computational demands. We introduce EffIntentGCN, a lightweight graph convolutional network designed to analyze pedestrian poses using a part-based graph and an adaptive graph, emphasizing features crucial for intention prediction and enhancing the learning of local and global interactions. An expansion layer added before each convolution block increases feature dimensionality for a broader representation, without excessive parameter increase. We further incorporate spatial-temporal joint attention, focusing on crucial joints in dynamic skeletal sequences. The model also integrates second-order information, representing joint connections, to provide complementary information. To balance model complexity with computational efficiency, essential for applications in resource-constrained environments like autonomous vehicles, we employ depthwise separable convolution and low-rank approximation techniques, aimed to reduce parameter count and maintain computational efficiency. The model is evaluated on the JAAD dataset, demonstrating its effectiveness in accurately predicting pedestrian crossing intentions while optimizing computational resources.

External IDs:dblp:journals/tits/ShiLII25