29 citations · 31 across the 12 of their papers we have counts for
12 papers
Hierarchical Decision Making with Structured Policies: A Principled Design via Inverse Optimization
Yuexuan Wang, Jingyuan Zhou, Kaidi Yang
Hierarchical decision-making frameworks are pivotal for addressing complex control tasks, enabling agents to decompose intricate problems into manageable subgoals. Despite their pr…
Neural Vector Lyapunov-Razumikhin Certificates for Delayed Interconnected Systems
Jingyuan Zhou, Yuexuan Wang, Kaidi Yang
Ensuring scalable input-to-state stability (sISS) is critical for the safety and reliability of large-scale interconnected systems, especially in the presence of communication dela…
Parameter Privacy-Preserving Data Sharing: A Particle-Belief MDP Formulation
Haokun Yu, Jingyuan Zhou, Kaidi Yang
This paper investigates parameter-privacy-preserving data sharing in continuous-state dynamical systems, where a data owner designs a data-sharing policy to support downstream esti…
Neural Cooperative Reach-While-Avoid Certificates for Interconnected Systems
Jingyuan Zhou, Haoze Wu, Kaidi Yang
Providing formal guarantees for neural network-based controllers in large-scale interconnected systems remains a fundamental challenge. In particular, using neural certificates to…
Platoon-Centric Green Light Optimal Speed Advisory Using Safe Reinforcement Learning
Ruining Yang, Jingyuan Zhou, Qiqing Wang +2
With recent advancements in Connected Autonomous Vehicles (CAVs), Green Light Optimal Speed Advisory (GLOSA) emerges as a promising eco-driving strategy to reduce the number of sto…
Scalable Synthesis and Verification of String Stable Neural Certificates for Interconnected Systems
Jingyuan Zhou, Haoze Wu, Haokun Yu +1
Ensuring string stability is critical for the safety and efficiency of large-scale interconnected systems. Although learning-based controllers (e.g., those based on reinforcement l…
Interaction-Aware Parameter Privacy-Preserving Data Sharing in Coupled Systems via Particle Filter Reinforcement Learning
Haokun Yu, Jingyuan Zhou, Kaidi Yang
This paper addresses the problem of parameter privacy-preserving data sharing in coupled systems, where a data provider shares data with a data user but wants to protect its sensit…
Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control
Jingyuan Zhou, Longhao Yan, Jinhao Liang +1
It is recognized that the control of mixed-autonomy platoons comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) can enhance traffic flow. Among exi…
Interaction-Aware Trajectory Prediction for Safe Motion Planning in Autonomous Driving: A Transformer-Transfer Learning Approach
Jinhao Liang, Chaopeng Tan, Longhao Yan +3
A critical aspect of safe and efficient motion planning for autonomous vehicles (AVs) is to handle the complex and uncertain behavior of surrounding human-driven vehicles (HDVs). D…
Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning
Jingyuan Zhou, Longhao Yan, Kaidi Yang
Connected and automated vehicles (CAVs) have recently gained prominence in traffic research due to advances in communication technology and autonomous driving. Various longitudinal…
A Parameter Privacy-Preserving Strategy for Mixed-Autonomy Platoon Control
Jingyuan Zhou, Kaidi Yang
It has been demonstrated that leading cruise control (LCC) can improve the operation of mixed-autonomy platoons by allowing connected and automated vehicles (CAVs) to make longitud…
Control-Aware Trajectory Predictions for Communication-Efficient Drone Swarm Coordination in Cluttered Environments
Longhao Yan, Jingyuan Zhou, Kaidi Yang
Swarms of Unmanned Aerial Vehicles (UAV) have demonstrated enormous potential in many industrial and commercial applications. However, before deploying UAVs in the real world, it i…