1. Specification

• DNN behavior: How to describe the DNN behavior?
• Dataset specification: How to specify the training and test data to argue a full coverage of the input space?

2. Data and DNN Architecture Selection

• Synthetic data and data augmentation: How can synthetic data and augmentation help make Deep Networks safe?
• Special DNN design: How can special DNN design increase the trustworthiness of DNN model output?
• DNN redundancy strategies: How to incorporate redundancy in DNN architectural design (e.g. sensor fusion, ensemble concepts)?

3. Training

• Transparent DNN training: How do models extract knowledge from training data and how to incorporate a-priori knowledge in training data?
• New loss functions: What new loss functions can help focusing on certain safety aspects?
• Methods for meta classification: What is the effectiveness of meta classifiers (e.g. based on uncertainty modeling or heat maps)?
• Robustness to anomalies: How to Increase robustness to anomalies in input data and how to defend adversarial attacks?
• Robustness across domains: How to Increase robustness of AI algorithms throughout different domains/datasets?

4. Evaluation / Testing

• Novel evaluation schemes: What novel evaluation schemes are meaningful for safe AI in automated driving?
• Interpretable and explainable: What diagnostic techniques can provide insight into the function and intermediate feature maps / layers?
• Evaluation of diagnostic techniques: How to evaluate and compare techniques for interpreting and explaining DNNs?

5. Monitoring

• Uncertainty modeling: How to model uncertainties during inference (e.g. via Monte Carlo dropout)?
• Detection of anomalies: How to detect anomalies in the input data (e.g. adversarial attacks, out-of-distribution examples)?
• Plausibility check of the DNN output: How to check the DNN output for plausibility (e.g. implausible positions and sizes of objects)?

Keynote Speakers