Towards Large Scale Urban Traffic Reference Data: Smart Infrastructure in the Test Area Autonomous Driving Baden-Württemberg

Fleck, Tobias; Daaboul, Karam; Weber, Michael; Schörner, Philip; Wehmer, Marek; Doll, Jens; Orf, Stefan; Sußmann, Nico; Hubschneider, Christian; Zofka, Marc René; Klunker, Florian; Kohlhaas, Ralf; Baumgart, Ingmar; Zöllner, R.; Zöllner, J. Marius

doi:10.1007/978-3-030-01370-7_75

Cited by 23 publications

(13 citation statements)

References 19 publications

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“…While we are generally also interested in behavior on German roads, there is no large-scale motion data set with corresponding map data that has been recorded in Germany. While we are aware of motion datasets (partly) recorded in Germany, such as [22]- [24], these are insufficient in size and variety for our quantification approach. However, since the traffic laws in the USA are similar to those in Germany, we deem the Waymo dataset appropriate, keeping the domain gap small.…”

Section: Methodsmentioning

confidence: 99%

Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules

Bogdoll,

Nekolla,

Joseph

et al. 2022

Preprint

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Driving on roads is restricted by various traffic rules, aiming to ensure safety for all traffic participants. However, human road users usually do not adhere to these rules strictly, resulting in varying degrees of rule conformity. Such deviations from given rules are key components of today's road traffic. In autonomous driving, robotic agents can disturb traffic flow, when rule deviations are not taken into account. In this paper, we present an approach to derive the distribution of degrees of rule conformity from human driving data. We demonstrate our method with the Waymo Open Motion dataset and Safety Distance and Speed Limit rules.

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Section: Methodsmentioning

confidence: 99%

Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules

Bogdoll,

Nekolla,

Joseph

et al. 2022

Preprint

Self Cite

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“…Stationary infrastructure sensors are sensors that are mounted on buildings, streetlamps, gantries, bridges, or other nearby structures. They are used in urban locations for example in the Ko-PER dataset [48], in the test area autonomous driving Baden-Württemberg [71], and at the AIM test site [156]. Infrastructure sensors at highways or motorways are used in Ref.…”

Section: Reference Data From Stationary Infrastructure Sensorsmentioning

confidence: 99%

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

2022

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Safety assurance of automated driving systems must consider uncertain environment perception. This paper reviews literature addressing how perception testing is realized as part of safety assurance. The paper focuses on testing for verification and validation purposes at the interface between perception and planning, and structures the analysis along the three axes (1) test criteria and metrics, (2) test scenarios, and (3) reference data. Furthermore, the analyzed literature includes related safety standards, safety-independent perception algorithm benchmarking, and sensor modeling. It is found that the realization of safety-oriented perception testing remains an open issue since challenges concerning the three testing axes and their interdependencies currently do not appear to be sufficiently solved.

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“…Junctions as critical hotspots will thus benefit in particular from infrastructure assistance, and represent an interesting use case for plausibility-related fault evaluation. In this section, we study an urban intersection scenario that is guided by the real infrastructure testbed in [36] (with igure 6: Plausibility signatures derived from the two Lidar sensors covering the intersection shown in Fig. 7.…”

Section: B Scenario 2: Urban Intersection With Lidarmentioning

confidence: 99%

“…Based on the Unreal Engine for the rendering, it provides with a set of towns and maps in addition to various sensor models such as RGB cameras, depth sensors, and Lidar sensors. For this scenario, a fourway intersection of the Town03 map that resembles the setup in [36] was chosen, see Fig. 7.…”

Section: B Scenario 2: Urban Intersection With Lidarmentioning

confidence: 99%

A Plausibility-Based Fault Detection Method for High-Level Fusion Perception Systems

Geißler

Unnervik

Paulitsch

2020

IEEE Open J. Intell. Transp. Syst.

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Trustworthy environment perception is the fundamental basis for the safe deployment of automated agents such as self-driving vehicles or intelligent robots. The problem remains that such trust is notoriously difficult to guarantee in the presence of systematic faults, e.g. non-traceable errors caused by machine learning functions. One way to tackle this issue without making rather specific assumptions about the perception process is plausibility checking. Similar to the reasoning of human intuition, the final outcome of a complex black-box procedure is verified against given expectations of an object's behavior. In this article, we apply and evaluate collaborative, sensor-generic plausibility checking as a mean to detect empirical perception faults from their statistical fingerprints. Our real use case is next-generation automated driving that uses a roadside sensor infrastructure for perception augmentation, represented here by test scenarios at a German highway and a city intersection. The plausibilization analysis is integrated naturally in the object fusion process, and helps to diagnose known and possibly yet unknown faults in distributed sensing systems.

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Towards Large Scale Urban Traffic Reference Data: Smart Infrastructure in the Test Area Autonomous Driving Baden-Württemberg

Cited by 23 publications

References 19 publications

Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules

Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

A Plausibility-Based Fault Detection Method for High-Level Fusion Perception Systems

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