Ensuring the quality of automated driving systems is a major challenge the automotive industry faces. In this context, quality defines the degree to which an object meets expectations and requirements. Especially, highly automated and automated vehicles at SAE levels 4 and 5 will be expected to operate safely in various contexts and complex situations without misconduct. Thus, a systematic approach is needed to show their safe operation. A way to address this challenge is simulation-based testing, as pure physical testing is not feasible for automated driving at levels 4 and 5 since several billion kilometers of driving are necessary. During simulation-based testing, the data used to evaluate the actual quality of an automated driving system are generated using a simulation. However, to rely on these simulation data, the overall simulation, including its simulation models, must provide a certain quality level. This quality level depends on the intended purpose for which the generated simulation data should be used. Therefore, three categories of quality can be considered: simulation quality (e.g., reliable simulation tool), quality of the automated driving system (e.g., handling of dangerous situations), and scenario quality (e.g., scenario criticality). Quality must be determined and evaluated in various process steps in developing and testing automated driving systems, the overall simulation, and the simulation models used for the simulation. In this paper, we propose a conceptual taxonomy to better understand quality in the development and testing process to have a clear separation and insight where further testing is needed – both in terms of automated driving systems and simulation, including their simulation models and scenarios used for testing.

Due to connectivity and automation trends, the medical device industry is experiencing increased demand for safety and security mechanisms. Anomaly detection has proven to be a valuable approach for ensuring safety and security in other industries, such as automotive or IT. Medical devices must operate across a wide range of values due to variations in patient anthropometric data, making anomaly detection based on a simple threshold for signal deviations impractical. For example, surgical robots directly contacting the patient’s tissue require precise sensor data. However, since the deformation of the patient’s body during interaction or movement is highly dependent on body mass, it is impossible to define a single threshold for implausible sensor data that applies to all patients. This also involves statistical methods, such as Z-score, that consider standard deviation. Even pure machine learning algorithms cannot be expected to provide the required accuracy simply due to the lack of available training data. This paper proposes using hybrid filters by combining dynamic system models based on expert knowledge and data-based models for anomaly detection in an operating room scenario. This approach can improve detection performance and explainability while reducing the computing resources needed on embedded devices, enabling a distributed approach to anomaly detection.

Context: Automotive software architectures describe distributed functionality by an interaction of software components. One drawback of today’s architectures is their strong integration into the onboard communication network based on predefined dependencies at design time. The idea is to reduce this rigid integration and technological dependencies. To this end, service-oriented architecture offers a suitable methodology since network communication is dynamically established at run-time. Aim: We target to provide a methodology for analysing hardware resources and synthesising automotive service-oriented architectures based on platform-independent service models. Subsequently, we focus on transforming these models into a platform-specific architecture realisation process following AUTOSAR Adaptive. Approach: For the platform-independent part, we apply the concepts of design space exploration and simulation to analyse and synthesise deployment configurations, i. e., mapping services to hardware resources at an early development stage. We refine these configurations to AUTOSAR Adaptive software architecture models representing the necessary input for a subsequent implementation process for the platform-specific part. Result: We present deployment configurations that are optimal for the usage of a given set of computing resources currently under consideration for our next generation of E/E architecture. We also provide simulation results that demonstrate the ability of these configurations to meet the run time requirements. Both results helped us to decide whether a particular configuration can be implemented. As a possible software toolchain for this purpose, we finally provide a prototype. Conclusion: The use of models and their analysis are proper means to get there, but the quality and speed of development must also be considered.

Modern vehicles increasingly rely on electronics, software, and communication technologies (cyber space) to perform their driving task. Over-The-Air (OTA) connectivity further extends the cyber space by creating remote access entry points. Accordingly, the vehicle is exposed to security attacks that are able to impact road safety. A profound understanding of security attacks, vulnerabilities, and mitigations is necessary to protect vehicles against cyber threats. While automotive threat descriptions, such as in UN R155, are still abstract, this creates a risk that potential vulnerabilities are overlooked and the vehicle is not secured against them. So far, there is no common understanding of the relationship of automotive attacks, the concrete vulnerabilities they exploit, and security mechanisms that would protect the system against these attacks. In this article, we aim at closing this gap by creating a mapping between UN R155, Microsoft STRIDE classification, Common Attack Pattern Enumeration and Classification (CAPEC), and Common Weakness Enumeration (CWE). In this way, already existing detailed knowledge of attacks, vulnerabilities, and mitigations is combined and linked to the automotive domain. In practice, this refines the list of UN R155 threats and therefore supports vehicle manufacturers, suppliers, and approval authorities to meet and assess the requirements for vehicle development in terms of cybersecurity. Overall, 204 mappings between UN threats, STRIDE, CAPEC attack patterns, and CWE weaknesses were created. We validated these mappings by applying our Automotive Attack Database (AAD) that consists of 361 real-world attacks on vehicles. Furthermore, 25 additional attack patterns were defined based on automotive-related attacks.

Prevailing trends in the automotive and medical device industry, such as life cycle overarching configurability, connectivity, and automation, require an adaption of development processes, especially regarding the security and safety thereof. The changing requirements imply that interfaces are more exposed to the outside world, making them more vulnerable to cyberattacks or data leaks. Consequently, not only do development processes need to be revised but also cybersecurity countermeasures and a focus on safety, as well as privacy, have become vital. While vehicles are especially exposed to cybersecurity and safety risks, the medical devices industry faces similar issues. In the automotive industry, proposals and draft regulations exist for security-related risk assessment processes. The medical device industry, which has less experience in these topics and is more heterogeneous, may benefit from drawing inspiration from these efforts. We examined and compared current standards, processes, and methods in both the automotive and medical industries. Based on the requirements regarding safety and security for risk analysis in the medical device industry, we propose the adoption of methods already established in the automotive industry. Furthermore, we present an example based on an interoperable Operating Room table (OR table).

Compared to traditional vehicles, battery electric vehicles (BEVs) have a limited driving range. Therefore, accurately estimating the range of BEVs is an important requirement to eliminate range anxiety, which describes the driver’s fear of getting stranded. However, range estimators used in currently available BEVs are not accurate enough. To overcome this problem, more precise energy estimation techniques have been investigated. Modeling the energy consumption of BEVs is essential to obtaining an accurate estimation. For accurately estimating the energy consumption, many non-deterministic influencing factors such as weather and traffic conditions, driving style, and the travel route need to be considered. Thus, reducing the possible feature space to improve estimation is necessary. In consequence, we propose a fully automatic methodology to select and extract a subset of energy-relevant features. Utilizing existing real-world data to investigate all types of influencing factors. Taking into account different segmentation methods, data scalers, feature selection, and extraction techniques, our methodology uses the full range of combinations to identify the combination that yields the best subset of features.