Search Results for author: Xuemin Hu

Found 5 papers, 0 papers with code

DiffPoGAN: Diffusion Policies with Generative Adversarial Networks for Offline Reinforcement Learning

no code implementations13 Jun 2024 Xuemin Hu, Shen Li, Yingfen Xu, Bo Tang, Long Chen

Offline reinforcement learning (RL) can learn optimal policies from pre-collected offline datasets without interacting with the environment, but the sampled actions of the agent cannot often cover the action distribution under a given state, resulting in the extrapolation error issue.

D4RL Offline RL +2

Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

no code implementations27 Mar 2024 Xuemin Hu, Pan Chen, Yijun Wen, Bo Tang, Long Chen

Reinforcement learning (RL) has been widely used in decision-making tasks, but it cannot guarantee the agent's safety in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving.

Autonomous Driving Decision Making +2

FusionPlanner: A Multi-task Motion Planner for Mining Trucks via Multi-sensor Fusion

no code implementations14 Aug 2023 Siyu Teng, Luxi Li, Yuchen Li, Xuemin Hu, Lingxi Li, Yunfeng Ai, Long Chen

Firstly, we propose a multi-task motion planning algorithm, called FusionPlanner, for autonomous mining trucks by the multi-sensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation.

Motion Planning Scheduling +1

How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligence

no code implementations2 May 2023 Xuemin Hu, Shen Li, Tingyu Huang, Bo Tang, Rouxing Huai, Long Chen

In general, a large scale of testing in simulation environment is conducted and then the learned driving knowledge is transferred to the real world, so how to adapt driving knowledge learned in simulation to reality becomes a critical issue.

Autonomous Driving

Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

no code implementations17 Mar 2023 Siyu Teng, Xuemin Hu, Peng Deng, Bai Li, Yuchen Li, Dongsheng Yang, Yunfeng Ai, Lingxi Li, Zhe XuanYuan, Fenghua Zhu, Long Chen

Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value.

Autonomous Driving Motion Planning

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