Ontology based Scene Creation for the Development of Automated Vehicles

The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.Comment: Accepted at the 2018 IEEE Intelligent Vehicles Symposium, 8 pages, 10 figure

A Comprehensive Review on Ontologies for Scenario-based Testing in the Context of Autonomous Driving

The verification and validation of autonomous driving vehicles remains a major challenge due to the high complexity of autonomous driving functions. Scenario-based testing is a promising method for validating such a complex system. Ontologies can be utilized to produce test scenarios that are both meaningful and relevant. One crucial aspect of this process is selecting the appropriate method for describing the entities involved. The level of detail and specific entity classes required will vary depending on the system being tested. It is important to choose an ontology that properly reflects these needs. This paper summarizes key representative ontologies for scenario-based testing and related use cases in the field of autonomous driving. The considered ontologies are classified according to their level of detail for both static facts and dynamic aspects. Furthermore, the ontologies are evaluated based on the presence of important entity classes and the relations between them

One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving

The core obstacle towards a large-scale deployment of autonomous vehicles currently lies in the long tail of rare events. These are extremely challenging since they do not occur often in the utilized training data for deep neural networks. To tackle this problem, we propose the generation of additional synthetic training data, covering a wide variety of corner case scenarios. As ontologies can represent human expert knowledge while enabling computational processing, we use them to describe scenarios. Our proposed master ontology is capable to model scenarios from all common corner case categories found in the literature. From this one master ontology, arbitrary scenario-describing ontologies can be derived. In an automated fashion, these can be converted into the OpenSCENARIO format and subsequently executed in simulation. This way, also challenging test and evaluation scenarios can be generated.Comment: Daniel Bogdoll and Stefani Guneshka contributed equally. Accepted for publication at ECCV 2022 SAIAD worksho

Discovery and Selection of Certified Web Services Through Registry-Based Testing and Verification

Reliability and trust are fundamental prerequisites for the establishment of functional relationships among peers in a Collaborative Networked Organisation (CNO), especially in the context of Virtual Enterprises where economic benefits can be directly at stake. This paper presents a novel approach towards effective service discovery and selection that is no longer based on informal, ambiguous and potentially unreliable service descriptions, but on formal specifications that can be used to verify and certify the actual Web service implementations. We propose the use of Stream X-machines (SXMs) as a powerful modelling formalism for constructing the behavioural specification of a Web service, for performing verification through the generation of exhaustive test cases, and for performing validation through animation or model checking during service selection

Ontology-based corner case scenario simulation for autonomous driving

Safety assessment of autonomous driving functions is an emerging topic in the automotive industry. In order to launch an autonomous vehicle on the road, the system running it needs to be able to react adequately in most of the cases, just like a human driver, if not even better. Furthermore, the amount of existing data that contains corner cases is nowhere near the amount of data needed to sufficiently train autonomous driving systems. The identification, classification and generation of corner cases for autonomous driving is a crucial part of scenario-based validation. In today’s world, a method that describes, generates and classifies those at the same time, is not available. In this thesis, a method for the description and generation of corner cases for autonomous driving is proposed. The method uses a template ontology, as a base for the scenario generation of corner cases with established definitions. The proposed approach also allows combining already described scenarios into new ones without any further actions. For an easy usage of the template ontology, an OntologyGenerator library for the simplified generation of corner cases is provided. The library completely eliminates the need for the user to work directly with the ontology and provides a method for creating scenarios even for users with little or no experience with ontologies. Moreover, the proposed approach is clean and understandable, making it easily scalable and extensible. The technique, follows the structure of OpenSCENARIO, thus making it easy to use, if one already understands OpenSCENARIO. In addition, the proposed ontology provides the base for the classification of the described scenarios to a specific corner case category. With the proposed technique, I was able to describe and generate seven scenarios, one for each of the earlier mentioned corner case categories. The resulting ontologies(created using the template ontology), that describe different corner cases, deliver correctly defined Open- SCENARIO files, after going through the Onto2OpenSCENARIOConverter. The resulted files deliver a correct simulation within CARLA, corresponding to the scenario description, which shows that the proposed method functions as expected

Measurable Safety of Automated Driving Functions in Commercial Motor Vehicles

With the further development of automated driving, the functional performance increases resulting in the need for new and comprehensive testing concepts. This doctoral work aims to enable the transition from quantitative mileage to qualitative test coverage by aggregating the results of both knowledge-based and data-driven test platforms. The validity of the test domain can be extended cost-effectively throughout the software development process to achieve meaningful test termination criteria