# Autoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems
## 2 Dependability Cage Approach: A Bird's Eye View  
To tackle the challenges of engineering-dependent autonomous systems, the dependability cage concept has been proposed [6; 13; 14; 15; 16]. Dependability cages are derived by engineers from existing development artifacts. The core concept of the approach is a continuous monitoring framework, as shown in Figure 3.  
For developing AVs, a common high-level functional architecture has been established, which includes three parts: (1) environmental and self-perception, (2) situation comprehension and action decision, and (3) trajectory planning and vehicle control [17; 18; 19].  
The monitor framework focuses on two issues: (a) shows the system correct behavior in terms of dependability requirements (_qualitative monitor_) and (b) operates the system in a situation and environment that has been trained or tested during development (_quantitative monitor_) (cf. Figure 3).  
The monitors both require consistent and abstract data access to the system under consideration, with the help of the _input_ and _output abstraction_ components. They depict the interface between the autonomous system and the two monitors. The input and output abstraction components both use defined interfaces to access the autonomous system's data and transform it into abstract representations. Abstract representation types and values are derived from the requirements specification and dependability criteria of the autonomous system.  
The qualitative monitor evaluates the correctness and safety of the system's behavior under the assumption that the system operates in a situation and environment that conform to the requirements specification, and it consists of an abstract behavior function and a conformity oracle. The abstract behavior function calculates a set of correct and safe actions in real time for the system in the current abstract situation. The conformity oracle compares the output abstraction with the set of correct and safe abstract actions from the abstract behavior function.  
The quantitative monitor observes the encountered abstract situations. For each situation, the monitor evaluates in real time if the encountered abstract situation is already known from development. A knowledge base provides information about tested situations on an abstract level. A canonical representation of abstract situations is used in this study. These canonic abstract situations are considered to be unique situations.  
If one of the monitors detects incorrect and unsafe system behavior, safety measures must be initiated to guarantee dependability requirements. The _fail-operational reaction_ component must transfer the corrupted system into a safe state with acceptable risk. For instance, one fail-operational reaction can be a graceful degradation of the autonomous system, as described in [20].  
Moreover, in the case that safety measures are initiated, system data will be automatically recorded. The recorded data can be transferred back into system development to identify and to resolve previously unknown faulty system behavior as well as to extend the scope of system tests. The qualitative monitor has already been successfully applied and evaluated using an industrial prototype of a highway pilot and data collected from road tests on German highways [13; 15].  
To realize a quantitative monitor, we need an efficient and effective solution to identify unknown sensor input data. As previously mentioned, we claim and will show in the following sections that our proposed semantic novelty detection approach can be used to successfully realize a quantitative monitor for a perception system.