Now showing 1 - 2 of 2
  • Publication
    Intelligent Testing for Autonomous Vehicles - Methods and Tools
    In this talk, I first give a tutorial on some fundamental AI testing methods with their strengths and weaknesses. For testing complex autonomous driving systems, an intelligent combination of basic AI testing techniques makes it possible to generate highly diversified test cases while enabling efficient bug hunting.
  • Publication
    Selected Challenges in ML Safety for Railway
    Neural networks (NN) have been introduced in safety-critical applications from autonomous driving to train inspection. I argue that to close the demo-to-product gap, we need scientifically-rooted engineering methods that can efficiently improve the quality of NN. In particular, I consider a structural approach (via GSN) to argue the quality of neural networks with NN-specific dependability metrics. A systematic analysis considering the quality of data collection, training, testing, and operation allows us to identify many unsolved research questions: (1) Solve the denominator/edge case problem with synthetic data, with quantifiable argumentation (2) Reach the performance target by combining classical methods and data-based methods in vision (3) Decide the threshold (for OoD or any kind) based on the risk appetite (societally accepted risk).