19 citations · 88 across the 27 of their papers we have counts for
5 papers · 1 filter
TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios
Lan Feng, Quanyi Li, Zhenghao Peng +2
Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous driving systems in simulation. This work introduces a data-driven method called Traff…
Human-AI Shared Control via Policy Dissection
Quanyi Li, Zhenghao Peng, Haibin Wu +2
Human-AI shared control allows human to interact and collaborate with AI to accomplish control tasks in complex environments. Previous Reinforcement Learning (RL) methods attempt t…
Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining
Qihang Zhang, Zhenghao Peng, Bolei Zhou
Deep visuomotor policy learning, which aims to map raw visual observation to action, achieves promising results in control tasks such as robotic manipulation and autonomous driving…
SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition
Mingxin Huang, Yuliang Liu, Zhenghao Peng +6
End-to-end scene text spotting has attracted great attention in recent years due to the success of excavating the intrinsic synergy of the scene text detection and recognition. How…
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
Quanyi Li, Zhenghao Peng, Bolei Zhou
Human intervention is an effective way to inject human knowledge into the training loop of reinforcement learning, which can bring fast learning and ensured training safety. Given…