83 citations · 85 across the 5 of their papers we have counts for
6 papers
V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception
Lei Yang, Xinyu Zhang, Jun Li +10
Modern autonomous vehicle perception systems often struggle with occlusions and limited perception range. Previous studies have demonstrated the effectiveness of cooperative percep…
Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework
Shiyu Fang, Jiaqi Liu, Mingyu Ding +4
At present, Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not sat…
Incorporating ESO into Deep Koopman Operator Modelling for Control of Autonomous Vehicles
Hao Chen, Chen Lv
Koopman operator theory is a kind of data-driven modelling approach that accurately captures the nonlinearities of mechatronic systems such as vehicles against physics-based method…
Deep Koopman Operator-Informed Safety Command Governor for Autonomous Vehicles
Hao Chen, Xiangkun He, Shuo Cheng +1
Modeling of nonlinear behaviors with physical-based models poses challenges. However, Koopman operator maps the original nonlinear system into an infinite-dimensional linear space…
Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation
Wenhui Huang, Yanxin Zhou, Xiangkun He +1
Despite some successful applications of goal-driven navigation, existing deep reinforcement learning (DRL)-based approaches notoriously suffers from poor data efficiency issue. One…
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration
Wenhui Huang, Cong Zhang, Jingda Wu +3
Massive practical works addressed by Deep Q-network (DQN) algorithm have indicated that stochastic policy, despite its simplicity, is the most frequently used exploration approach.…