Predicting pedestrian crossing intention to ensure pedestrian safety has garnered significant attention in autonomous driving. Balancing the accuracy and real-time performance of prediction is a challenging endeavor. This article introduces faster pedestrian crossing intention prediction network (Faster-PCPNet), a rapid pedestrian crossing intention prediction network that jointly predicts intention based on pedestrian pose, ego-vehicle speed, pedestrian bounding box, and the novel triple quasi-polar coordinate. Faster-PCPNet incorporates the temporal-channel sharing enhanced topology graph convolution (TCGC) module designed in this article, allowing for in-depth exploration of significant pedestrian crossing action features. This article innovatively establishes a quasi-polar coordinate system, addressing the challenge of efficiently representing pedestrian crossing intention during relative motion between pedestrians and vehicles. Faster-PCPNet achieves accuracy rates of 89% on the JAAD dataset and 94% on the PIE public dataset. The experimental results highlight the real-time performance and reliability of the proposed model on mobile devices in traffic scenarios, effectively mitigating traffic accidents caused by pedestrian crossing. Our code and model are available at https://github.com/zjrcczu/Faster-PCPNet.
Faster Pedestrian Crossing Intention Prediction Based on Efficient Fusion of Diverse Intention Influencing Factors
IEEE Transactions on Transportation Electrification ; 10 , 4 ; 9071-9087
2024-12-01
18353725 byte
Article (Journal)
Electronic Resource
English