Yongzhi Jiang — accepted AI-conference papers

2025

A Real-Time Spatio-Temporal Trajectory Planner for Autonomous Vehicles With Semantic Graph Optimization

Shan He, Yalong Ma, Tao Song, Yongzhi Jiang, Xinkai Wu

RA-L 2025

Planning a safe and feasible trajectory for autonomous vehicles in real-time by fully utilizing perceptual information in complex urban environments is challenging. In this letter, we propose a spatio-temporal trajectory planning method based on graph optimization. It efficiently extracts the multi-

Cited by 3 Source ↗Scholar ↗

2025

HGAT-CP: Heterogeneous Graph Attention Network for Collision Prediction in Autonomous Driving

Yongzhi Jiang, Bin Zhou, Yongwei Li, Xinkai Wu, Zhongxia Xiong

ICRA 2025

Predicting potential collision events is beneficial to ensure the driving safety of autonomous vehicles. Existing graph-based collision prediction methods rely heavily on domain knowledge and predefined semantic relations, limiting their flexibility and adaptability in complex driving scenarios. To

Cited by 0 Source ↗Scholar ↗