The automotive industry is increasing its effort towards scientific and technological innovations regarding autonomous vehicles. The expectation is a reduction of road accidents, which are too often caused by human errors. Moreover, technological solutions, such as connected autonomous vehicle platoons, are expected to help humans in emergency situations. In this context, safety and security issues do not yet have a satisfactory answer. In this paper, we address the domain of secure communication among vehicles – especially the issues related to authentication and authorization of inter-vehicular signals and services carrying safety commands. We propose a novel design methodology, where we take a contract-based approach for specifying safety, and combine it in the design flow with the use of the Arrowhead Framework to support security. Furthermore, we present the results through a demo, which employs model-based design for software implementation and the physical realization on autonomous model cars.

The PRISM (partial risk map) methodology is a novel risk assessment method developed as the combination of the failure mode and effect analysis and risk matrix risk assessment methods. Based on the concept of partial risks, three different aggregation functions are presented for assessing incident risks. Since the different aggregation functions give different properties to the obtained PRISM numbers and threshold surfaces (convex, concave, linear), the description of these properties is carried out. Similarity analyses based on the sum of ranking differences (SRD) method and rank correlation are performed and robustness tests are applied related to the changes of the assessment scale lengths. The PRISM method provides a solution for the systematically criticized problem of the FMEA, i.e., it is not able to deal with hidden risks behind the aggregated RPN number, while the method results in an expressive tool for risk management. Applying new aggregation functions, proactive assessment can be executed, and predictions can be given related to the incidents based on the nature of their hidden risk. The method can be suggested for safety science environments where human safety, environmental protection, sustainable production, etc., are highly required.

Control charts are widely used tools of statistical process control (SPC). They are effective tools for indicating process shifts, helping avoid an increased number of defective products in the manufacturing environment. However, their effectiveness is strongly dependent on the performance of the measurement system. In the scientific literature, numerous studies have examined the effect of measurement errors in statistical process control, but only a few of them have focused on developing risk-based control charts that consider the cost of decisions during process control; furthermore, they were developed only for the case of multivariate and adaptive control charts such as T ² and VSSI (Variable sample Size and Sampling Interval) X-bar charts. This paper fills this gap by using the proposed risk-based concept for commonly used control charts such as X-bar, Moving Average (MA), Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts. The effectiveness of the developed risk-based control charts is demonstrated by simulations and real-life datasets.