Jianfeng Yu — accepted AI-conference papers

2025

DPGP: A Hybrid 2D-3D Dual Path Potential Ghost Probe Zone Prediction Framework for Safe Autonomous Driving

Weiming Qu, Jiawei Du, Shenghai Yuan, Jia Wang, Yang Sun, Shengyi Liu +8

IROS 2025

Modern robots must coexist with humans in dense urban environments. A key challenge is the ghost probe problem, where pedestrians or objects unexpectedly rush into traffic paths. This issue affects both autonomous vehicles and human drivers. Existing works propose vehicle-to-everything (V2X) strateg

Cited by 2 Source ↗Scholar ↗

2025

SILM: A Subjective Intent Based Low-Latency Framework for Multiple Traffic Participants Joint Trajectory Prediction

Weiming Qu, Jia Wang, Jiawei Du, Yuanhao Zhu, Jianfeng Yu, Rui Xia +3

IROS 2025

Trajectory prediction is a fundamental technology for advanced autonomous driving systems and represents one of the most challenging problems in the field of cognitive intelligence. Accurately predicting the future trajectories of each traffic participant is a prerequisite for building high safety a

Cited by 0 Source ↗Scholar ↗