Capsule Networks for Hierarchical Novelty Detection in Object Classification

Hierarchical Novelty Detection (HND) refers to assigning labels to objects in a hierarchical category space, where non-leaf labeling represents a novelty detection of that category. By labeling a novel instance in at least one abstract category, more informed decisions can be made by an automated driving (AD) function, resulting in a safer behavior in novel situations. Current approaches are mainly composed of different architectures based on Convolutional Neural Networks (CNNs). Capsule Networks (CNs) were introduced as an alternative to CNNs that expand their capacity in tasks that were previously challenging. We explore the hierarchical nature of CNs and propose a novel approach for hierarchical novelty detection using a unified CN architecture. As a proof-of-concept, we evaluate it on a novelty detection task based on the Fashion-MNIST dataset. We define a misclassification matrix for evaluation of the performance based on a semantically sensible scenario for this dataset. The results show that our method outperforms the main CNN-based methods in the current literature in this task while also giving more flexibility for task-specific tuning and has the potential to reach state-of-the-art status in more complex HND use cases within the AD domain

Providing Evidence for the Validity of the Virtual Verification of Automated Driving Systems

With the increasing complexity of automated driving systems, formal verification as well as statistical verification that solely relies on real-world testing methods, become infeasible. Virtual testing seems like a promising alternative to traditional methods, especially as part of a scenario-based verification and validation methodology. But in order to transfer the test results of a system from a simulation to the real world, we need to argue the validity of the virtual tests. Our proposed method enables this validity argumentation by comparing the virtual test traces against traces that have sufficiently similar recorded real-world traces. To reduce the amount of required real-world data, the method involves two mechanisms to generalize the validity statement of a single real-world trace to a set of virtual traces. The reduction of required data is showcased in a proof of concept that compares the needed amounts of data with a "naive" validation method and here presented enhancements in an ablation study

Providing Evidence for the Validity of the Virtual Verification of Automated Driving Systems

With the increasing complexity of automated driving systems, formal verification as well as statistical verification that solely relies on real-world testing methods, become infeasible. Virtual testing seems like a promising alternative to traditional methods, especially as part of a scenario-based verification and validation methodology. But in order to transfer the test results of a system from a simulation to the real world, we need to argue the validity of the virtual tests. Our proposed method enables this validity argumentation by comparing the virtual test traces against traces that have sufficiently similar recorded real-world traces. To reduce the amount of required real-world data, the method involves two mechanisms to generalize the validity statement of a single real-world trace to a set of virtual traces. The reduction of required data is showcased in a proof of concept that compares the needed amounts of data with a "naive" validation method and here presented enhancements in an ablation study

Optimizing Neural Networks for Embedded Hardware

Neural networks are a pervasive technology, which is, however, still held back in the area of embedded systems by the high resource requirements, especially memory size, memory access time and power dissipation. In recent years, several different methods have been proposed to transform given neural networks in such a way that they can get by with much fewer resources while maintaining almost the same accuracy. This work reviews, categorizes and describes the state of the art in adapting and simplifying neural networks to make them better applicable to embedded systems. Even though we developed this study from a purely automotive context, the techniques described are also valid in other areas