Pedestrian intention recognition and action prediction from the perspective of vehicles
Predicting pedestrian crossing action is key for intelligent driving vehicles and is crucial to ensure the safety of pedestrians.Existing methods typically model pedestrian action using trajectories or postures,but pedestrian action is complex and variable.Without a deeper semantic interpretation of pedestrian behaviors,a better understanding of pedestrian behaviors can hardly be achieved.This paper investigates the integration of pedestrian crossing intentions and actions,and designs a multitask network to identify pedestrian intentions and predict pedestrian actions.It suggests pedestrian crossing intentions influence their actions and utilizes future pedestrian actions as prior information to detect current intentions and actions.Additionally,it considers the impact of surrounding traffic targets and vehicle movements on pedestrians,and designs a feature fusion module to integrate pedestrian features with traffic target features.Finally,our model is validated on two autonomous driving datasets (PIE and JAAD),showing superior results and demonstrating advantages in modeling pedestrian intentions and actions.
国家科技期刊平台
NSTL
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