High-Precision Digital Traffic Recording with Multi-LiDAR Infrastructure Sensor Setups

Kloeker, Laurent; Geller, Christian; Kloeker, Amarin; Eckstein, Lutz

doi:10.48550/arxiv.2006.12140

Cited by 1 publication

(2 citation statements)

References 16 publications

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“…VIII-C3. The projects HDVMess and ACCorD aim at installing mobile and fixed infrastructure sensors at various sites [159] and evaluate their data generation with a UAV-based reference system [160].…”

Section: Reference Data From Non-road Usersmentioning

confidence: 99%

“…The necessary processing steps to track ground vehicles from UAVs were later described in more detail in [161] and [162]. UAVs are used as reference sensors for sensor modeling in [110] and for assessing infrastructure sensors in [160]. A qualitative assessment of UAVbased reference systems in contrast to other reference systems is given in [163].…”

Section: Reference Data From Non-road Usersmentioning

confidence: 99%

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

Hoss,

Scholtes,

Eckstein

2021

Preprint

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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. We focus on testing for verification and validation purposes at the interface between perception and planning, and structure our 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, safetyindependent perception algorithm benchmarking, and sensor modeling. We find that the realization of safety-aware 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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Section: Reference Data From Non-road Usersmentioning

confidence: 99%

Section: Reference Data From Non-road Usersmentioning

confidence: 99%

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

Hoss,

Scholtes,

Eckstein

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

High-Precision Digital Traffic Recording with Multi-LiDAR Infrastructure Sensor Setups

Abstract: Fig. 1. Fused point cloud of a multi-LiDAR infrastructure sensor setup at an intersection with tracking results (red) and ground truth data (green), respectively, marked by oriented bounding boxes. The blue point cloud originates from eight LiDAR sensors.

Cited by 1 publication

References 16 publications

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

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

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