High-performance LiDAR systems play an important role in autonomous driving by providing a high-resolution 3D representation of the driving environment. Complementing version-based object detection, LiDAR systems must furnish reliable and appropriate information for the vehicle. Current LiDAR systems for vehicle applications prefer to be solid-state to achieve high system robustness. However, the lack of beam steering limits the beam flux density and thus the detection range of a solid-state LiDAR system. One of the main reasons is that the emitted irradiant flux must be distributed to a large number of pixels of a focal plane array detector, which results in a single pixel receiving only a small optical power. To increase the optical power reaching at the detector, this paper investigates the influence of different beam shaping methods on the detection range. Subsequently, an irradiation pattern to maximize the detection range for solid-state LiDAR systems is determined. Based on the determined irradiation pattern, we propose an optical concept for both the emitter and detector sides of the solid-state LiDAR system. 

Increasing the detection range of non-scanning solid-state LiDAR systems using beam shaping

AbstractIn engineering research, various sensors and data sources are utilized for data acquisition. Together with subsequent processing and analysis, a large, heterogeneous amount of data and information is generated. To structure data and information within the research project, intelligent tools for research data management (RDM) are required. However, existing tools for RDM focus on data management during a research project and lack of capabilities to describe a technical system over this life cycle in multiple projects. Thus this paper addresses the potential of the Digital Twin (DT) concept for RDM. Based on requirements from RDM we derive specifications for a DT concept in RDM and introduce three criteria to choose suitable technical systems for DT. We present a DT for a research vehicle, implemented within the open-source Industry 4.0 DT tool “AASX Package Explorer”, to show the benefits of using a DT for RDM. KeywordsDigital TwinResearch data managementField data 

Management of Research Field Data Within the Concept of Digital Twin

The advance of automated vehicles imposes increasing requirements on the sensor system of vehicles. Besides the ongoing development of perception algorithms, different hardware approaches exist in order to improve the detection of infrastructure and road users. In the far-field in front of the vehicle, the detection of infrastructure and road users relies on camera and LiDAR systems. However, the reliability of both systems is influenced by weather conditions, especially due to reflections from snow, rain, or fog and the camera by the ambient lighting as well. Optimized algorithms are implemented to improve the vision of both systems but limitations remain. RaDAR is proven to work more reliably in adverse weather conditions but struggles in providing sufficient data for detailed object classification. In combination with data fusion, the sensor systems can provide a partly redundant perception of the road and its users. This paper aims to provide a proof of concept for the improvement of the vision of camera systems in low light by using active NIR illumination. For this purpose, the spectral emission of visible and near-infrared sources is compared with the sensitivity of a camera. Considering regulatory emission limits, an optimal wavelength for additional NIR lighting is determined. Based on the determined wavelength we research the correlation between the output power of the sources and the camera’s perceived brightness and introduce possible applications for the additional NIR illumination

Hauke Dierend

Papers

Management of Research Field Data Within the Concept of Digital Twin

Active NIR illumination for improved camera view in automated driving application