# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 1 Introduction
It is essential to acknowledge if any portion of the available datasets is representative of the real-world settings that the systems will face; if not, the possibility of repurposing and adapting these instances should be studied. The current challenges of developing an application-oriented solution to in-vehicle monitoring have two distinct origins. On the one hand, there are potential issues that are directly linked to the characteristics of the application, which are specifically manifested by the absence of public datasets explicitly dedicated to in-vehicle monitoring. Additionally, the importance of actor independence in Shared Autonomous Vehicles, moving backgrounds and frequent illumination changes caused by the movement of the vehicle are important factors to consider. On the other hand, there are current limitations that are transversal to every anomaly detection technique that has been proposed. As [PERSON] et al. [6] denote, a series of complex detection challenges remain largely unsolved and are yet to be fully addressed by deep anomaly detection. The first of these challenges is the low anomaly detection recall rate caused by their rare and heterogeneous nature; as they are difficult to identify, sophisticated anomalies are missed. Additionally, since the candidate pool of anomalies is often unbounded, the strategies that these methods employ to deal with novelty must not be overlooked. As a consequence of the previously mentioned difficulty of gathering abnormal samples for training or validation, there is a significant effort to achieve high data efficiency for learning normality and abnormality. Fully supervised anomaly detection is, for the time being, a virtually impossible endeavour, mainly due to the high cost of collecting large-scale data or generating sufficiently broad artificial dataset solutions. When some labels for anomaly classes are available, they might be incomplete, inexact (e.g., coarse-grained) or inaccurate. The subject of actor independence is relevant as well, as the ones present in the training data could generate a bias due to their lack of representativity (e.g., height, gender, age, type of clothes). In addition to learning expressive representations with a small amount of data, it is also essential to learn models that are generalisable to novel anomalies. This theme also extends to noise-resilient anomaly detection, with noise being equivalent to mislabelled data or unlabelled anomalies. The amount of noise not only differs significantly from dataset to dataset, but it is also irregularly distributed in the data space. Noise-resilient models can leverage this incomplete data to achieve better performance and robustness. Most currently developed methods are committed to detecting individual instances that are anomalous, which are often regarded as point anomalies. However, more complex anomalies, such as conditional and group anomalies, comprise objectively different dynamics and behaviours. Conditional anomalies also refer to individual anomalous instances, but they only represent abnormal behaviour when they occur in a specific context. Group anomalies are anomalous as a whole, although the isolated behaviour of every member might not be abnormal. Furthermore, many applications require the detection of anomalies with multiple data sources, heterogeneous (e.g., video and audio) or not (e.g., multiple surveillance cameras). The complexity of these systems is yet to be properly addressed by deep anomaly detection strategies, even though high-dimensional anomaly detection has been a long-standing problem [7]. Identifying intricate feature interactions and couplings is already a challenge when temporal and spatial interdependency relationships are considered. The success of Machine Learning (ML) has led to a growing interest in the development of Artificial Intelligence (AI) applications capable of providing explanations to their decisions, which are often called Explainable AI [8]. This information is essential for users to trust, understand and manage these applications.