Advancements in Safety-Critical Systems and Noise Control

The field of safety-critical systems and noise control is moving towards more adaptive and robust solutions. Researchers are exploring new approaches to uncertainty modeling, latent safety filters, and motion planning to improve the performance and reliability of systems in various environments. A key direction is the development of methods that can adapt to changing conditions and uncertainty, such as runtime adaptation to user-specified safety constraints and disturbance recasting to enable safe deployment of offline-learned safety filters. Another important area of research is the prediction and mitigation of noise generated by urban air mobility systems, with a focus on developing certifiable noise models and noise-aware motion planning algorithms. Notable papers in this area include:

  • AnySafe, which proposes constraint-parameterized latent safety filters that can adapt to user-specified safety constraints at runtime.
  • SPACE2TIME, which enables safe and adaptive deployment of offline-learned safety filters under unknown, spatially-varying disturbances.
  • Certified Learning-Enabled Noise-Aware Motion Planning for Urban Air Mobility, which proposes a novel noise-aware motion planning framework for UAM systems that ensures compliance with noise regulations.

Sources

Data-Driven Uncertainty Modeling for Robust Feedback Active Noise Control in Headphones

AnySafe: Adapting Latent Safety Filters at Runtime via Safety Constraint Parameterization in the Latent Space

From Space to Time: Enabling Adaptive Safety with Learned Value Functions via Disturbance Recasting

Certified Learning-Enabled Noise-Aware Motion Planning for Urban Air Mobility

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