DPCIAN: A Novel Dual-Channel Pedestrian Crossing Intention Anticipation Network
Biao Yang, Zhiwen Wei, Hongyu Hu, Rui Wang, Changchun Yang, Rongrong Ni
Abstract
The increase in car ownership has improved the convenience of people’s travel, but it has also increased the potential risk of pedestrian-vehicle conflicts. In complex traffic scenarios, pedestrian crossing behavior may lead to frequent traffic accidents. It is crucial to accurately and timely anticipate pedestrian crossing intentions to avoid pedestrian-vehicle conflicts, improve driving safety and ensure pedestrian safety. We propose a novel dual-channel pedestrian crossing intention anticipation network (DPCIAN) to anticipate pedestrian crossing intentions. DPCIAN comprises the pedestrian action encoding module and the scene traffic object interaction relation module (STOIRM). Initially, the pedestrian action encoding module resorts to channel-refined Graph Convolutional networks (GCNs) to extract spatio-temporal action features from pedestrians’ skeletons, which improves the inflexible feature extraction of the adaptive GCN and the information redundancy of the multi-channel GCN. Afterward, the STOIRM introduces an interaction relation network to excavate scene object interaction features related to the target pedestrian’s crossing intention with better scene understanding. Finally, the adaptive average pooling layer is used to fuse the spatio-temporal and interaction features to achieve robust anticipation of pedestrian crossing intention. DPCIAN is evaluated on two public datasets, JAAD and PIE, with an accuracy of 89% and 91%, respectively. Both qualitative and quantitative evaluations indicate that DPCIAN can precisely anticipate pedestrians’ crossing intentions in complex traffic scenarios. Code is available at: https://github.com/wrysmile99/DPCIAN.