Case Study: Runtime Safety Verification of Neural Network Controlled System
<|reference_start|>Case Study: Runtime Safety Verification of Neural Network Controlled System: Neural networks are increasingly used in safety-critical applications such as robotics and autonomous vehicles. However, the deployment of neural-network-controlled systems (NNCSs) raises significant safety concerns. Many recent advances overlook critical aspects of verifying control and ensuring safety in real-time scenarios. This paper presents a case study on using POLAR-Express, a state-of-the-art NNCS reachability analysis tool, for runtime safety verification in a Turtlebot navigation system using LiDAR. The Turtlebot, equipped with a neural network controller for steering, operates in a complex environment with obstacles. We developed a safe online controller switching strategy that switches between the original NNCS controller and an obstacle avoidance controller based on the verification results. Our experiments, conducted in a ROS2 Flatland simulation environment, explore the capabilities and limitations of using POLAR-Express for runtime verification and demonstrate the effectiveness of our switching strategy.<|reference_end|>
@article{yang2024case,
    title={Case Study: Runtime Safety Verification of Neural Network Controlled
  System},
    author={Frank Yang, Sinong Simon Zhan, Yixuan Wang, Chao Huang, Qi Zhu},
    journal={arXiv preprint arXiv:2408.08592},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08592},
    primaryClass={cs.RO}
}