Ontology based Scene Creation for the Development of Automated Vehicles

Bagschik, Gerrit; Menzel, Till; Maurer, Markus

doi:10.1109/ivs.2018.8500632

Cited by 229 publications

(154 citation statements)

References 16 publications

Supporting

Mentioning

137

Contrasting

Order By: Relevance

“…As a first step, to generate functional scenarios, the available knowledge has to be structured, varied on a semantic level and described linguistically. Therefore, Bagschik et al [13] present a knowledge-based approach to generate start scenes using an ontology. To structure the knowledge represented in the ontology, Bagschik et al use a 5-layer-model based on the 4-layer-model by Schuldt et al [7].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

From Functional to Logical Scenarios: Detailing a Keyword-Based Scenario Description for Execution in a Simulation Environment

Menzel

Bagschik

Isensee

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

Self Cite

View full text Add to dashboard Cite

Scenario-based development and test processes are a promising approach for verifying and validating automated driving functions. For this purpose, scenarios have to be generated during the development process in a traceable manner. In early development stages, the operating scenarios of the item to be developed are usually described in an abstract, linguistic way. Within the scope of a simulation-assisted test process, these linguistically described scenarios have to be transformed into a state space representation and converted into data formats which can be used with the respective simulation environment. Currently, this step of detailing scenarios takes a considerable manual effort. Furthermore, a standardized interpretation of the linguistically described scenarios and a consistent transformation into the data formats are not guaranteed due to multiple authors as well as many constraints between the scenario parameters. In this paper, the authors present an approach to automatically detail a keyword-based scenario description for execution in a simulation environment and provide a basis for test case generation. As a first step, the keyword-based description is transformed into a parameter space representation. At the same time, constraints regarding the selection and combination of parameter values are documented for the following process steps (e. g. evolutionary or stochastic test methods). As a second step, the parameter space representation is converted into data formats required by the simulation environment. As an example, the authors use scenarios on German freeways and convert them into the data formats OpenDRIVE (description of the road) and OpenSCENARIO (description of traffic participants and environmental conditions) for execution in the simulation environment Virtual Test Drive.

show abstract

Section: Related Workmentioning

confidence: 99%

“…In the first step of detailing, as shown in Fig. 4, the terms used for scenario description are structured according to the 5-layer-model [13]. Afterwards, each term is augmented with parameters which specify the respective element in detail.…”

Section: A Scenario Detailingmentioning

confidence: 99%

From Functional to Logical Scenarios: Detailing a Keyword-Based Scenario Description for Execution in a Simulation Environment

Menzel

Bagschik

Isensee

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Layer 5 describes environmental variables that have an influence on the other layers; for example, rain lowering the friction coefficient in layer 1. The model initially consisting of 5 layers (Bagschik et al 2018b) was extended by a sixth layer, which covers digital information such as the availability of high-definition map data or vehicle-to-everything communication (Bock et al 2018).…”

Section: Scenario-based Validationmentioning

confidence: 99%

“…The first relies on a formal representation of knowledge using a model or other forms of knowledge representation. Examples of defining scenarios with a focus on the behavior of the participants based on a formal representation of knowledge can be found in Neumann and Novak (1986), Bagschik et al (2018aBagschik et al ( , 2018b, and Kemper and Etzien (2014); a model-based approach is applied in Bach et al (2016). To generate scenarios, these approaches typically utilize scenes with discrete positions on the road or a discrete set of maneuvers for each participant.…”

Section: Definition Of Scenariosmentioning

confidence: 99%

A framework for definition of logical scenarios for safety assurance of automated driving

Weber

Bock

Klimke

et al. 2019

Traffic Injury Prevention

View full text Add to dashboard Cite

Objective: In order to introduce automated vehicles on public roads, it is necessary to ensure that these vehicles are safe to operate in traffic. One challenge is to prove that all physically possible variations of situations can be handled safely within the operational design domain of the vehicle. A promising approach to handling the set of possible situations is to identify a manageable number of logical scenarios, which provide an abstraction for object properties and behavior within the situations. These can then be transferred into concrete scenarios defining all parameters necessary to reproduce the situation in different test environments. Methods: This article proposes a framework for defining safety-relevant scenarios based on the potential collision between the subject vehicle and a challenging object, which forces the subject vehicle to depart from its planned course of action to avoid a collision. This allows defining only safety-relevant scenarios, which can directly be related to accident classification. The first criterion for defining a scenario is the area of the subject vehicle with which the object would collide. As a second criterion, 8 different positions around the subject vehicle are considered. To account for other relevant objects in the scenario, factors that influence the challenge for the subject vehicle can be added to the scenario. These are grouped as action constraints, dynamic occlusions, and causal chains. Results: By applying the proposed systematics, a catalog of base scenarios for a vehicle traveling on controlled-access highways has been generated, which can directly be linked to parameters in accident classification. The catalog serves as a basis for scenario classification within the PEGASUS project. Conclusions: Defining a limited number of safety-relevant scenarios helps to realize a systematic safety assurance process for automated vehicles. Scenarios are defined based on the point of the potential collision of a challenging object with the subject vehicle and its initial position. This approach allows defining scenarios for different environments and different driving states of the subject vehicle using the same mechanisms. A next step is the generation of logical scenarios for other driving states of the subject vehicle and for other traffic environments. ARTICLE HISTORY

show abstract

“…Therefore, the long test driving distances needed for statistical validation should be significantly reduced by the identification of challenging or critical scenarios that can be reproduced in simulation or on test fields. Critical scenarios are identified by metrics (Junietz et al 2017) or created automatically (e.g., Bagschik et al 2018) and can be stored in a central scenario database (P€ utz et al 2017). Scenarios can be described on different levels of detail :…”

Section: Scenario-based Approachmentioning

confidence: 99%

Functional decomposition—A contribution to overcome the parameter space explosion during validation of highly automated driving

Amersbach

Winner

2019

Traffic Injury Prevention

View full text Add to dashboard Cite

Objective: Particular testing by functional decomposition of the automated driving function can potentially contribute to reducing the effort of validating highly automated driving functions. In this study, the required size of test suites for scenario-based testing and the potential to reduce it by functional decomposition are quantified for the first time. Methods: The required size of test suites for scenario-based approval of a so-called Autobahn-Chauffeur (SAE Level 3) is analyzed for an exemplary set of scenarios. Based on studies of data from failure analyses in other domains, the possible range for the required test coverage is narrowed down and suitable discretization steps, as well as ranges for the influence parameters, are assumed. Based on those assumptions, the size of the test suites for testing the complete system is quantified. The effects that lead to a reduction in the parameter space for particular testing of the decomposed driving function are analyzed and the potential to reduce the validation effort is estimated by comparing the resulting test suite sizes for both methods. Results: The combination of all effects leads to a reduction in the test suites' size by a factor between 20 and 130, depending on the required test coverage. This means that the size of the required test suite can be reduced by 95-99% by particular testing compared to scenario-based testing of the complete system. Conclusions: The reduction potential is a valuable contribution to overcome the parameter space explosion during the validation of highly automated driving. However, this study is based on assumptions and only a small set of exemplary scenarios. Thus, the findings have to be validated in further studies. ARTICLE HISTORY

show abstract

Ontology based Scene Creation for the Development of Automated Vehicles

Cited by 229 publications

References 16 publications

From Functional to Logical Scenarios: Detailing a Keyword-Based Scenario Description for Execution in a Simulation Environment

From Functional to Logical Scenarios: Detailing a Keyword-Based Scenario Description for Execution in a Simulation Environment

A framework for definition of logical scenarios for safety assurance of automated driving

Functional decomposition—A contribution to overcome the parameter space explosion during validation of highly automated driving

Contact Info

Product

Resources

About