Identifying relevant traffic situations based on human decision making

Sippl, Christoph; Bock, Florian; Huber, Beate; Djanatliev, Anatoli; German, Reinhard

doi:10.1007/978-3-658-25939-6_79

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…Alongside the discussion of the research results, further architectural potentials of the concept are also assessed. In the next step, it should be investigated how and to what extent the proposed approach can support the validation of behavior models, the exploratory identification of scenarios [28] or the development of novel communication concepts [43].…”

Section: Discussionmentioning

confidence: 99%

“…To test automated driving functions, the traffic scenarios are described in a machine-readable manner and the function under test is exposed to these scenarios using an environment simulation [25][26][27]. Furthermore, exploratory environment simulations can help to identify critical scenarios for the function under development during early development stages, when requirements are not yet fully defined [28]. Therefore, the components of a traffic scenario have been structured into five layers, which are depicted in Figure 1.…”

Section: Literature and Requirements Reviewmentioning

confidence: 99%

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Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems

2020

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Automated driving technologies offer the opportunity to substantially reduce the number of road accidents and fatalities. This requires the development of systems that can handle traffic scenarios more reliable than the human driver. The extreme number of traffic scenarios, though, causes enormous challenges in testing and proving the correct system functioning. Due to its efficiency and reproducibility, the test procedure will involve environment simulations to which the system under test is exposed. A combination of traffic, driving and Vulnerable Road User (VRU) simulation is therefore required for a holistic environment simulation. Since these simulators have different requirements and support various formats, a concept for integrated spatio-semantic road space modeling is proposed in this paper. For this purpose, the established standard OpenDRIVE, which describes road networks with their topology for submicroscopic driving simulation and HD maps, is combined with the internationally used semantic 3D city model standard CityGML. Both standards complement each other, and their combination opens the potentials of both application domains—automotive and 3D GIS. As a result, existing HD maps can now be used by model processing tools, enabling their transformation to the target formats of the respective simulators. Based on this, we demonstrate a distributed environment simulation with the submicroscopic driving simulator Virtual Test Drive and the pedestrian simulator MomenTUM at a sensitive crossing in the city of Ingolstadt. Both simulators are coupled at runtime and the architecture supports the integration of automated driving functions.

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Section: Discussionmentioning

confidence: 99%

Section: Literature and Requirements Reviewmentioning

confidence: 99%

Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems

2020

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“…For a scenario-based validation methodology, however, a set of scenarios is essential [2]. Although several studies attempt to identify scenarios [3], [4], [5], [6], [7] and describing them appropriately [8], this topic is still an open issue and demands further investigation.…”

Section: Introductionmentioning

confidence: 99%

Identification of Lane-Change Maneuvers in Real-World Drivings With Hidden Markov Model and Dynamic Time Warping

Klitzke

Koch

Köster

2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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For the introduction of new automated driving functions, the systems need to be verified extensively. A scenario-driven approach has become an accepted method for this task. But to verify the functionality of an automated vehicle in the simulation in a certain scenario such as a lane change, characteristics of scenarios need to be identified. This, however, requires to extract lane-change from real-world drivings accurately. For that purpose, this work proposes a novel framework for lane-change identification by combining multiple unsupervised learning methods. To represent various types of lane changes, the maneuver is split up into primitive driving actions with an Hidden Markov Model (HMM) and Divisive Hierarchical Clustering (DHC). Based on them, lane change maneuvers are identifier using Dynamic Time Warping (DTW). The presented framework is evaluated with a realworld test drive and compared to other baseline methods. With a F1 score of 98.01% in lane-change identification, the presented approach outperforms the other approaches.

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Identifying relevant traffic situations based on human decision making

Cited by 2 publications

References 13 publications

Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems

Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems

Identification of Lane-Change Maneuvers in Real-World Drivings With Hidden Markov Model and Dynamic Time Warping

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