5 papers · 1 filter
Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos
Yuxuan Xie, Nicolas Pugeault, Chongfeng Wei +2
Pedestrian trajectory prediction from an on-board ego-centric camera is challenging since it depends on complex interactions with vehicles and scene context, as well as the intenti…
DYNA-PRUNER: Input-Adaptive Data-Model Co-Pruning for Efficient and Scalable Spatio-Temporal Media Prediction
Fuyan Zhang, Yuqi Li, Qing Xu +2
Spatio-temporal prediction supports radar/satellite nowcasting and city-scale traffic monitoring, but modern models are often too expensive for real-time deployment. This stems fro…
ESIA: An Energy-Based Spatiotemporal Interaction-Aware Framework for Pedestrian Intention Prediction
Yanping Wu, Meiting Dang, Lin Wu +3
Recent advances in autonomous driving have motivated research on pedestrian intention prediction, which aims to infer future crossing decisions and actions by modeling temporal dyn…
Modeling 3D Pedestrian-Vehicle Interactions for Vehicle-Conditioned Pose Forecasting
Guangxun Zhu, Xuan Liu, Nicolas Pugeault +2
Accurately predicting pedestrian motion is crucial for safe and reliable autonomous driving in complex urban environments. In this work, we present a 3D vehicle-conditioned pedestr…
Waymo-3DSkelMo: A Multi-Agent 3D Skeletal Motion Dataset for Pedestrian Interaction Modeling in Autonomous Driving
Guangxun Zhu, Shiyu Fan, Hang Dai +1
Large-scale high-quality 3D motion datasets with multi-person interactions are crucial for data-driven models in autonomous driving to achieve fine-grained pedestrian interaction u…