{"title":"Decouple Ego-View Motions for Predicting Pedestrian Trajectory and Intention","authors":"Zhengming Zhang;Zhengming Ding;Renran Tian","doi":"10.1109/TIP.2024.3445734","DOIUrl":null,"url":null,"abstract":"Pedestrian trajectory prediction is a critical component of autonomous driving in urban environments, allowing vehicles to anticipate pedestrian movements and facilitate safer interactions. While egocentric-view-based algorithms can reduce the sensing and computation burdens of 3D scene reconstruction, accurately predicting pedestrian trajectories and interpreting their intentions from this perspective requires a better understanding of the coupled vehicle (camera) and pedestrian motions, which has not been adequately addressed by existing models. In this paper, we present a novel egocentric pedestrian trajectory prediction approach that uses a two-tower structure and multi-modal inputs. One tower, the vehicle module, receives only the initial pedestrian position and ego-vehicle actions and speed, while the other, the pedestrian module, receives additional prior pedestrian trajectory and visual features. Our proposed action-aware loss function allows the two-tower model to decompose pedestrian trajectory predictions into two parts, caused by ego-vehicle movement and pedestrian movement, respectively, even when only trained on combined ego-view motions. This decomposition increases model flexibility and provides a better estimation of pedestrian actions and intentions, enhancing overall performance. Experiments on three publicly available benchmark datasets show that our proposed model outperforms all existing algorithms in ego-view pedestrian trajectory prediction accuracy.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10648593/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Pedestrian trajectory prediction is a critical component of autonomous driving in urban environments, allowing vehicles to anticipate pedestrian movements and facilitate safer interactions. While egocentric-view-based algorithms can reduce the sensing and computation burdens of 3D scene reconstruction, accurately predicting pedestrian trajectories and interpreting their intentions from this perspective requires a better understanding of the coupled vehicle (camera) and pedestrian motions, which has not been adequately addressed by existing models. In this paper, we present a novel egocentric pedestrian trajectory prediction approach that uses a two-tower structure and multi-modal inputs. One tower, the vehicle module, receives only the initial pedestrian position and ego-vehicle actions and speed, while the other, the pedestrian module, receives additional prior pedestrian trajectory and visual features. Our proposed action-aware loss function allows the two-tower model to decompose pedestrian trajectory predictions into two parts, caused by ego-vehicle movement and pedestrian movement, respectively, even when only trained on combined ego-view motions. This decomposition increases model flexibility and provides a better estimation of pedestrian actions and intentions, enhancing overall performance. Experiments on three publicly available benchmark datasets show that our proposed model outperforms all existing algorithms in ego-view pedestrian trajectory prediction accuracy.