Xuewu Ji — accepted AI-conference papers

2023

Graph-Based Scenario-Adaptive Lane-Changing Trajectory Planning for Autonomous Driving

Qing Dong, Zhanhong Yan, Kimihiko Nakano, Xuewu Ji, Yahui Liu

RA-L 2023

Trajectory planning is one of the key challenges to the rapid and large-scale deployment of autonomous driving. The lane-changing trajectory planning algorithm for autonomous driving is typically formulated as a optimization process of a cost function, which can be challenging to manually tune for d

Cited by 13 Source ↗Scholar ↗

2020

Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets

Cong Fei, Bin Wang, Yuzheng Zhuang, Zongzhang Zhang, Jianye Hao, Hongbo Zhang +2

IJCAI 2020poster

Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning. However, the requirement of isolated single modal demonstrations limits the scalability of the approach to real world scenarios…

Cited by 0 Source ↗PDF ↗Scholar ↗