593 citations · 1.8k across the 57 of their papers we have counts for
15 papers · 1 filter
Scalable Synthesis of Formally Verified Neural Value Function for Hamilton-Jacobi Reachability Analysis
Yujie Yang, Hanjiang Hu, Tianhao Wei +2
Hamilton-Jacobi (HJ) reachability analysis provides a formal method for guaranteeing safety in constrained control problems. It synthesizes a value function to represent a long-ter…
Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving
Yuqi Dai, Jian Sun, Shengbo Eben Li +4
Perception is essential for autonomous driving system. Recent approaches based on Bird's-eye-view (BEV) and deep learning have made significant progress. However, there exists chal…
Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning
Yuxuan Jiang, Yujie Yang, Zhiqian Lan +6
Rocket recycling is a crucial pursuit in aerospace technology, aimed at reducing costs and environmental impact in space exploration. The primary focus centers on rocket landing co…
Risk-Aware Vehicle Trajectory Prediction Under Safety-Critical Scenarios
Qingfan Wang, Dongyang Xu, Gaoyuan Kuang +3
Trajectory prediction is significant for intelligent vehicles to achieve high-level autonomous driving, and a lot of relevant research achievements have been made recently. Despite…
Convolutional Unscented Kalman Filter for Multi-Object Tracking with Outliers
Shiqi Liu, Wenhan Cao, Chang Liu +2
Multi-object tracking (MOT) is an essential technique for navigation in autonomous driving. In tracking-by-detection systems, biases, false positives, and misses, which are referre…
Controllability Test for Nonlinear Datatic Systems
Yujie Yang, Letian Tao, Likun Wang +1
Controllability is a fundamental property of control systems, serving as the prerequisite for controller design. While controllability test is well established in modelic (i.e., mo…
Diffusion Actor-Critic with Entropy Regulator
Yinuo Wang, Likun Wang, Yuxuan Jiang +8
Reinforcement learning (RL) has proven highly effective in addressing complex decision-making and control tasks. However, in most traditional RL algorithms, the policy is typically…
The Feasibility Theory of Constrained Reinforcement Learning: A Tutorial Study
Yujie Yang, Zhilong Zheng, Masayoshi Tomizuka +2
Satisfying safety constraints is a priority concern when solving optimal control problems (OCPs). Due to the existence of infeasibility phenomenon, where a constraint-satisfying so…
Convolutional Bayesian Filtering
Wenhan Cao, Shiqi Liu, Chang Liu +3
Bayesian filtering serves as the mainstream framework of state estimation in dynamic systems. Its standard version utilizes total probability rule and Bayes' law alternatively, whe…
Policy Bifurcation in Safe Reinforcement Learning
Wenjun Zou, Yao Lyu, Jie Li +7
Safe reinforcement learning (RL) offers advanced solutions to constrained optimal control problems. Existing studies in safe RL implicitly assume continuity in policy functions, wh…
Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments
Haicheng Liao, Shangqian Liu, Yongkang Li +5
In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. Traditional approaches often…
Canonical Form of Datatic Description in Control Systems
Guojian Zhan, Ziang Zheng, Shengbo Eben Li
The design of feedback controllers is undergoing a paradigm shift from modelic (i.e., model-driven) control to datatic (i.e., data-driven) control. Canonical form of state space mo…
A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving
Haicheng Liao, Yongkang Li, Zhenning Li +4
In autonomous vehicle (AV) technology, the ability to accurately predict the movements of surrounding vehicles is paramount for ensuring safety and operational efficiency. Incorpor…
On the Stability of Datatic Control Systems
Yujie Yang, Zhilong Zheng, Shengbo Eben Li
The development of feedback controllers is undergoing a paradigm shift from (model-driven) control to (data-driven) control. Stability, as a f…
Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
Yinan Zheng, Jianxiong Li, Dongjie Yu +4
Safe offline RL is a promising way to bypass risky online interactions towards safe policy learning. Most existing methods only enforce soft constraints, i.e., constraining safety…