Grasping Causality for the Explanation of Criticality for Automated Driving

The verification and validation of automated driving systems at SAE levels 4 and 5 is a multi-faceted challenge for which classical statistical considerations become infeasible. For this, contemporary approaches suggest a decomposition into scenario classes combined with statistical analysis thereof regarding the emergence of criticality. Unfortunately, these associational approaches may yield spurious inferences, or worse, fail to recognize the causalities leading to critical scenarios, which are, in turn, prerequisite for the development and safeguarding of automated driving systems. As to incorporate causal knowledge within these processes, this work introduces a formalization of causal queries whose answers facilitate a causal understanding of safety-relevant influencing factors for automated driving. This formalized causal knowledge can be used to specify and implement abstract safety principles that provably reduce the criticality associated with these influencing factors. Based on Judea Pearl's causal theory, we define a causal relation as a causal structure together with a context, both related to a domain ontology, where the focus lies on modeling the effect of such influencing factors on criticality as measured by a suitable metric. As to assess modeling quality, we suggest various quantities and evaluate them on a small example. As availability and quality of data are imperative for validly estimating answers to the causal queries, we also discuss requirements on real-world and synthetic data acquisition. We thereby contribute to establishing causal considerations at the heart of the safety processes that are urgently needed as to ensure the safe operation of automated driving systems

Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving

Knowledge representation and reasoning has a long history of examining how knowledge can be formalized, interpreted, and semantically analyzed by machines. In the area of automated vehicles, recent advances suggest the ability to formalize and leverage relevant knowledge as a key enabler in handling the inherently open and complex context of the traffic world. This paper demonstrates ontologies to be a powerful tool for a) modeling and formalization of and b) reasoning about factors associated with criticality in the environment of automated vehicles. For this, we leverage the well-known 6-Layer Model to create a formal representation of the environmental context. Within this representation, an ontology models domain knowledge as logical axioms, enabling deduction on the presence of critical factors within traffic scenarios. For executing automated analyses, a joint description logic and rule reasoner is used in combination with an a-priori predicate augmentation. We elaborate on the modular approach, present a publicly available implementation, and exemplarily evaluate the method by means of a large-scale drone data set of urban traffic scenarios

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

The large-scale deployment of automated vehicles on public roads has the potential to vastly change the transportation modalities of today's society. Although this pursuit has been initiated decades ago, there still exist open challenges in reliably ensuring that such vehicles operate safely in open contexts. While functional safety is a well-established concept, the question of measuring the behavioral safety of a vehicle remains subject to research. One way to both objectively and computationally analyze traffic conflicts is the development and utilization of so-called criticality metrics. Contemporary approaches have leveraged the potential of criticality metrics in various applications related to automated driving, e.g. for computationally assessing the dynamic risk or filtering large data sets to build scenario catalogs. As a prerequisite to systematically choose adequate criticality metrics for such applications, we extensively review the state of the art of criticality metrics, their properties, and their applications in the context of automated driving. Based on this review, we propose a suitability analysis as a methodical tool to be used by practitioners. Both the proposed method and the state of the art review can then be harnessed to select well-suited measurement tools that cover an application's requirements, as demonstrated by an exemplary execution of the analysis. Ultimately, efficient, valid, and reliable measurements of an automated vehicle's safety performance are a key requirement for demonstrating its trustworthiness

Towards a Congruent Interpretation of Traffic Rules for Automated Driving - Experiences and Challenges

The homologation of automated driving systems for public roads requires a rigorous safety case. Regulations of the United Nations demand to demonstrate the compliance of the developed system with local traffic rules. Hence, evidences for this have to be delivered by means of formal proofs, online monitoring, and other verification techniques in the safety case. In order for such methods to be applicable traffic rules have to be made machine-interpretable. However, that pursuit is highly challenging. This work reports on our practical experiences regarding the formalization of a non-trivial part of the German road traffic act. We identify a central issue when formalizing traffic rules within a development process, coined as the congruence problem, which is concerned with the semantic equality of the legal and system interpretation of traffic rules. As our main contribution, we delineate potential challenges arising from the congruence problem, hence impeding a congruent yet formal interpretation of traffic rules. Finally, we aim to initiate discussions by highlighting steps to partially address these challenges

Inter-ictal assay of peripheral circulating inflammatory mediators in migraine patients under adjunctive cervical non-invasive vagus nerve stimulation (nVNS) : A proof-of-concept study

Objective: To assay peripheral inter-ictal cytokine serum levels and possible relations with non-invasive vagus nerve stimulation (nVNS) responsiveness in migraineurs. Methods: This double-blinded, sham-controlled study enrolled 48 subjects and measured headache severity, frequency [headache days/month, number of total and mild/moderate/severe classified attacks/month], functional state [sleep, mood, body weight, migraine-associated disability] and serum levels of inflammatory markers [inter-ictal] using enzyme-linked immunoassays at baseline and after 2 months of adjunctive nVNS compared to sham stimulation and suitably matched controls. Results: No significant differences were observed at baseline and after 2 months for headache severity, total attacks/month, headache days/month and functional outcome [sleep, mood, disability] between verum and sham nVNS. However, the number of severe attacks/month significantly decreased in the verum nVNS group and circulating pro-inflammatory IL-1 beta was elevated significantly in the sham group compared to nVNS. Levels of anti-inflammatory IL-10 were significantly higher at baseline in both groups compared to healthy controls, but not at 2 months follow-up [p 0.05]. No severe device-/stimulation-related adverse events occurred. Conclusion: 2 months of adjunctive cervical nVNS significantly declined the number of severe attacks/month. Pro-inflammatory IL-1 beta plasma levels [inter-ictal] were higher in sham-treated migraine patients compared to verum nVNS. However, pro- [IL-6, HMGB-1, TNF-alpha, leptin] and anti-inflammatory [IL-10, adiponectin, ghrelin] mediators did not differ statistically. Profiling of neuroinflammatory circuits in migraine to predict nVNS responsiveness remains an experimental approach, which may be biased by pre-analytic variables warranting large-scale biobank-based systematic investigations [omics]. (C) 2019 Elsevier Inc. All rights reserved.Peer reviewe