Our goal is to advance safe and reliable solutions that meet the growing needs of modern autonomous systems, by integrating machine learning, control theory, and optimization.

Ongoing Research Areas

Fault-Tolerant Autonomous Systems

Developing innovative techniques to enhance fault tolerance in autonomous systems, focusing on both model-based and data-driven methods for detecting and addressing abnormal behaviors.

  • Research Focus:
    • Model-based fault estimation and fault-tolerant control
    • Hybrid integration of learning and control for real-time fault management
  • Key Publications:
    • J. Lan, and R. Patton, “Robust Integration of Model-based Fault Estimation and Fault-Tolerant Control,” Springer, 2020.
    • J. Lan, “Safety Monitoring and Alert for Neural Network-Enabled Control Systems”, IFAC World Congress, IFAC-PapersOnLine, 56(2), 9436-9441, 2023.

Intelligent Transportation Systems

Aiming to secure future transportation networks, our research addresses safety, robustness, and cooperative control in connected vehicle systems.

  • Research Focus:
    • Data-driven approaches to cooperative adaptive cruise control (CACC)
    • Safe and efficient mixed traffic systems
  • Key Publications:
    • J. Lan, “Data-driven cooperative adaptive cruise control for unknown nonlinear vehicle platoons,” IET Intelligent Transport Systems, 2024.
    • J. Lan, et al., “Safe and robust data‐driven cooperative control policy for mixed vehicle platoons,” International Journal of Robust and Nonlinear Control, 2022.
    • J. Lan, et al., “Data-Driven Robust Predictive Control for Mixed Vehicle Platoons using Noisy Measurement”, IEEE Transactions on Intelligent Transportation Systems, 24(6): 6586 – 6596, 2023.

Safe AI and Autonomous Systems

We focus on developing methods to ensure the safety, robustness, and effectiveness of AI in autonomous systems, with an emphasis on runtime monitoring, formal verification, and safe reinforcement learning.

  • Research Focus:
    • Robust and explainable AI for critical decision-making
    • Runtime monitoring of learning-enabled systems
  • Key Publications:
    • J. Jiang, J. Lan, et al., “Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation,” PMLR, 2024.
    • J. Lan, et al., “Runtime Monitoring and Fault Detection for Neural Network-Controlled Systems,” IFAC Safeprocess, IFAC-PapersOnLine, 2024.

Vision-Based Autonomous Robots

Our work integrates advanced perception and control systems for autonomous robots, addressing challenges in vision, path planning, and control under dynamic conditions.

  • Research Focus:
    • SLAM (Simultaneous Localization and Mapping) for dynamic environments
    • Collision-free navigation with lightweight vision-based frameworks
  • Key Publications:
    • Z. Lin, et al., “SLAM²: Simultaneous Localization and Multimode Mapping for Indoor Dynamic Environments,” Pattern Recognition, 2025.
    • Z. Lin, et al., “Enhanced Visual SLAM for Collision-Free Driving with Lightweight Autonomous Cars,” Sensors, 2024.

For a comprehensive list of our work, please visit our publications page.