Eike Möhlmann

Nadja Marko, Eike Möhlmann, Dejan Ničković et al.

After more than a decade of intense focus on automated vehicles, we are still facing huge challenges for the vision of fully autonomous driving to become a reality. The same "disillusionment" is true in many other domains, in which autonomous Cyber-Physical Systems (CPS) could considerably help to overcome societal challenges and be highly beneficial to society and individuals. Taking the automotive domain, i.e. highly automated vehicles (HAV), as an example, this paper sets out to summarize the major challenges that are still to overcome for achieving safe, secure, reliable and trustworthy highly automated resp. autonomous CPS. We constrain ourselves to technical challenges, acknowledging the importance of (legal) regulations, certification, standardization, ethics, and societal acceptance, to name but a few, without delving deeper into them as this is beyond the scope of this paper. Four challenges have been identified as being the main obstacles to realizing HAV: Realization of continuous, post-deployment systems improvement, handling of uncertainties and incomplete information, verification of HAV with machine learning components, and prediction. Each of these challenges is described in detail, including sub-challenges and, where appropriate, possible approaches to overcome them. By working together in a common effort between industry and academy and focusing on these challenges, the authors hope to contribute to overcome the "disillusionment" for realizing HAV.

Christian Neurohr, Lukas Westhofen, Tabea Henning et al.

The homologation of automated vehicles, being safety-critical complex systems, requires sound evidence for their safe operability. Traditionally, verification and validation activities are guided by a combination of ISO 26262 and ISO/PAS 21448, together with distance-based testing. Starting at SAE Level 3, such approaches become infeasible, resulting in the need for novel methods. Scenario-based testing is regarded as a possible enabler for verification and validation of automated vehicles. Its effectiveness, however, rests on the consistency and substantiality of the arguments used in each step of the process. In this work, we sketch a generic framework around scenario-based testing and analyze contemporary approaches to the individual steps. For each step, we describe its function, discuss proposed approaches and solutions, and identify the underlying arguments, principles and assumptions. As a result, we present a list of fundamental considerations for which evidences need to be gathered in order for scenario-based testing to support the homologation of automated vehicles.