An Ontology-based Approach to Generate the Advanced Driver Assistance Use Cases of Highway Traffic

Chen, Wei; Kloul, Leïla

doi:10.5220/0006931700750083

Cited by 30 publications

(17 citation statements)

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“…This is a crucial advantage compared to simpler approaches like pure combinatorial testing. Chen and Kloul [41] combine a motorway, a weather and a vehicle ontology and also model the relationships between the three ontologies. Bagschik et al [9] concentrate on German highways and represent all five layers of their environmental model.…”

Section: Knowledge-based Methodsmentioning

confidence: 99%

Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles

et al. 2020

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Due to the rapid progress in the development of automated vehicles over the last decade, their market entry is getting closer. One of the remaining challenges is the safety assessment and type approval of automated vehicles, as conventional testing in the real world would involve an unmanageable mileage. Scenario-based testing using simulation is a promising candidate for overcoming this approval trap. Although the research community has recognized the importance of safeguarding in recent years, the quality of simulation models is rarely taken into account. Without investigating the errors and uncertainties of models, virtual statements about vehicle safety are meaningless. This paper describes a whole process combining model validation and safety assessment. It is demonstrated by means of an actual type-approval regulation that deals with the safety assessment of lane-keeping systems. Based on a thorough analysis of the current state-of-the-art, this paper introduces two approaches for selecting test scenarios. While the model validation scenarios are planned from scratch and focus on scenario coverage, the type-approval scenarios are extracted from measurement data based on a data-driven pipeline. The deviations between lane-keeping behavior in the real and virtual world are quantified using a statistical validation metric. They are then modeled using a regression technique and inferred from the validation experiments to the unseen virtual type-approval scenarios. Finally, this paper examines safety-critical lane crossings, taking into account the modeling errors. It demonstrates the potential of the virtual-based safeguarding process using exemplary simulations and real driving tests.

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Section: Knowledge-based Methodsmentioning

confidence: 99%

Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles

et al. 2020

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“…Chen and Kloul [54] combine three ontologies as a knowledge base for the generation of motorway scenarios: a motorway, a weather-based and a vehicle ontology, each connected by relations and effects. Furthermore, traffic rules are expressed as first-order logic.…”

Section: Knowledge-based Scenario Generationmentioning

confidence: 99%

Survey on Scenario-Based Safety Assessment of Automated Vehicles

et al. 2020

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When will automated vehicles come onto the market? This question has puzzled the automotive industry and society for years. The technology and its implementation have made rapid progress over the last decade, but the challenge of how to prove the safety of these systems has not yet been solved. Since a market launch without proof of safety would neither be accepted by society nor by legislators, much time and many resources have been invested into safety assessment in recent years in order to develop new approaches for an efficient assessment. This paper therefore provides an overview of various approaches, and gives a comprehensive survey of the so-called scenario-based approach. The scenario-based approach is a promising method, in which individual traffic situations are typically tested by means of virtual simulation. Since an infinite number of different scenarios can theoretically occur in real-world traffic, even the scenario-based approach leaves the question unanswered as to how to break these down into a finite set of scenarios, and find those which are representative in order to render testing more manageable. This paper provides a comprehensive literature review of related safety-assessment publications that deal precisely with this question. Therefore, this paper develops a novel taxonomy for the scenario-based approach, and classifies all literature sources. Based on this, the existing methods will be compared with each other and, as one conclusion, the alternative concept of formal verification will be combined with the scenario-based approach. Finally, future research priorities are derived.

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“…Bagschik et al [6] present a method for knowledge-based scene creation, where the description of the environment is based on the fivelayer model of Schuldt [9]. Most ontologies in the context of traffic scene description use or are oriented towards the five level model and represent a scene using the Resource Description Framework (RDF) [10] [11]. The focus of the ontologies is limited to the spatial composition of the scene.…”

Section: State Of the Artmentioning

confidence: 99%

Towards Traffic Scene Description: The Semantic Scene Graph

Zipfl¹,

Zöllner²

2021

Preprint

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For the classification of traffic scenes, a description model is necessary that can describe the scene in a uniform way, independent of its domain. A model to describe a traffic scene in a semantic way is described in this paper. The description model allows to describe a traffic scene independently of the road geometry and road topology. Here, the traffic participants are projected onto the road network and represented as nodes in a graph. Depending on the relative location between two traffic participants with respect to the road topology, semantic classified edges are created between the corresponding nodes. For concretization, the edge attributes are extended by relative distances and velocities between both traffic participants with regard to the course of the lane. An important aspect of the description is that it can be converted easily into a machinereadable format. The current description focuses on dynamic objects of a traffic scene and considers traffic participants, such as pedestrians or vehicles.

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An Ontology-based Approach to Generate the Advanced Driver Assistance Use Cases of Highway Traffic

Cited by 30 publications

References 0 publications

Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles

Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Towards Traffic Scene Description: The Semantic Scene Graph

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