Automated vehicles use light detection and ranging (LiDAR) sensors for environmental scanning. However, the relative motion between the scanning LiDAR sensor and objects leads to a distortion of the point cloud. This phenomenon is known as the motion distortion effect, significantly degrading the sensor’s object detection capabilities and generating false negative or false positive errors. In this work, we have introduced ray tracing-based deterministic and analytical approaches to model the motion distortion effect on the scanning LiDAR sensor’s performance for simulation-based testing. In addition, we have performed dynamic test drives at a proving ground to compare real LiDAR data with the motion distortion effect simulation data. The real-world scenarios, the environmental conditions, the digital twin of the scenery, and the object of interest (OOI) are replicated in the virtual environment of commercial software to obtain the synthetic LiDAR data. The real and the virtual test drives are compared frame by frame to validate the motion distortion effect modeling. The mean absolute percentage error (MAPE), the occupied cell ratio (OCR), and the Barons cross-correlation coefficient (BCC) are used to quantify the correlation between the virtual and the real LiDAR point cloud data. The results show that the deterministic approach matches the real measurements better than the analytical approach for the scenarios in which the yaw rate of the ego vehicle changes rapidly.

Modern LiDAR sensors find increasing use in safety-critical applications. Therefore, highly accurate modeling of the system's behavior under demanding environmental conditions is necessary. In this paper, we present a modular structure to accurately simulate the amplified raw detector signal of a direct time-of-flight LiDAR system for coaxial transmitter-receiver optics using a silicon photomultiplier (SiPM) as a detector. The SiPM causes strong non-linearities of the response due to its response to subsequent light pulses. To verify the model's predictions, single-point measurements for targets of different reflectivity at defined distances were performed. Statistical analysis shows an R-squared value greater than 0.990 for simulated and measured signal amplitude levels. Noise modeling shows good accordance with the performed measurements for different target irradiance levels. The presented results have a guiding significance in the modeling of the complex signal processing chain of LiDAR systems based on SiPM detectors, as it enables the prediction of key parameters of the system early in the development process. Hence, unnecessary costs by design flaws can be mitigated.

Many modern automated vehicle sensor systems use light detection and ranging (LiDAR) sensors. The prevailing technology is scanning LiDAR, where a collimated laser beam illuminates objects sequentially point-by-point to capture 3D range data. In current systems, the point clouds from the LiDAR sensors are mainly used for object detection. To estimate the velocity of an object of interest (OoI) in the point cloud, the tracking of the object or sensor data fusion is needed. Scanning LiDAR sensors show the motion distortion effect, which occurs when objects have a relative velocity to the sensor. Often, this effect is filtered, by using sensor data fusion, to use an undistorted point cloud for object detection. In this study, we developed a method using an artificial neural network to estimate an object’s velocity and direction of motion in the sensor’s field of view (FoV) based on the motion distortion effect without any sensor data fusion. This network was trained and evaluated with a synthetic dataset featuring the motion distortion effect. With the method presented in this paper, one can estimate the velocity and direction of an OoI that moves independently from the sensor from a single point cloud using only one single sensor. The method achieves a root mean squared error (RMSE) of 0.1187 m s−1 and a two-sigma confidence interval of [−0.0008 m s−1, 0.0017 m s−1] for the axis-wise estimation of an object’s relative velocity, and an RMSE of 0.0815 m s−1 and a two-sigma confidence interval of [0.0138 m s−1, 0.0170 m s−1] for the estimation of the resultant velocity. The extracted velocity information (4D-LiDAR) is available for motion prediction and object tracking and can lead to more reliable velocity data due to more redundancy for sensor data fusion.

Measurement performance evaluation of real and virtual automotive light detection and ranging (LiDAR) sensors is an active area of research. However, no commonly accepted automotive standards, metrics, or criteria exist to evaluate their measurement performance. ASTM International released the ASTM E3125-17 standard for the operational performance evaluation of 3D imaging systems commonly referred to as terrestrial laser scanners (TLS). This standard defines the specifications and static test procedures to evaluate the 3D imaging and point-to-point distance measurement performance of TLS. In this work, we have assessed the 3D imaging and point-to-point distance estimation performance of a commercial micro-electro-mechanical system (MEMS)-based automotive LiDAR sensor and its simulation model according to the test procedures defined in this standard. The static tests were performed in a laboratory environment. In addition, a subset of static tests was also performed at the proving ground in natural environmental conditions to determine the 3D imaging and point-to-point distance measurement performance of the real LiDAR sensor. In addition, real scenarios and environmental conditions were replicated in the virtual environment of a commercial software to verify the LiDAR model's working performance. The evaluation results show that the LiDAR sensor and its simulation model under analysis pass all the tests specified in the ASTM E3125-17 standard. This standard helps to understand whether sensor measurement errors are due to internal or external influences. We have also shown that the 3D imaging and point-to-point distance estimation performance of LiDAR sensors significantly impacts the working performance of the object recognition algorithm. That is why this standard can be beneficial in validating automotive real and virtual LiDAR sensors, at least in the early stage of development. Furthermore, the simulation and real measurements show good agreement on the point cloud and object recognition levels.

In this work, we introduce a novel approach to model the rain and fog effect on the light detection and ranging (LiDAR) sensor performance for the simulation-based testing of LiDAR systems. The proposed methodology allows for the simulation of the rain and fog effect using the rigorous applications of the Mie scattering theory on the time domain for transient and point cloud levels for spatial analyses. The time domain analysis permits us to benchmark the virtual LiDAR signal attenuation and signal-to-noise ratio (SNR) caused by rain and fog droplets. In addition, the detection rate (DR), false detection rate (FDR), and distance error derror of the virtual LiDAR sensor due to rain and fog droplets are evaluated on the point cloud level. The mean absolute percentage error (MAPE) is used to quantify the simulation and real measurement results on the time domain and point cloud levels for the rain and fog droplets. The results of the simulation and real measurements match well on the time domain and point cloud levels if the simulated and real rain distributions are the same. The real and virtual LiDAR sensor performance degrades more under the influence of fog droplets than in rain.

Modern LiDAR sensors find increasing use in safety-critical applications. Therefore, highly accurate modeling of the system's behavior under demanding environmental conditions is necessary. In this paper, we present a modular structure to accurately simulate the amplified raw detector signal of a direct time-of-flight LiDAR system for coaxial transmitter-receiver optics. Our model describes, a measurement system based on standard optical components and a detector able of converting single photons to an electrical signal. To verify the model's predictions, single-point measurements for targets of different reflectivity at defined distances were performed. Statistical analysis shows an R-squared value greater than 0.990 for simulated and measured signal amplitude levels. Noise modeling shows good accordance with the performed measurements for different target irradiance levels. The presented results have a guiding significance in the modeling of the complex signal processing chain of LiDAR systems, as it enables the prediction of key parameters of the system early in the development process. Hence, unnecessary costs by design flaws can be mitigated. The modular structure allows easy adaption for arbitrary LiDAR systems.

Autonomous vehicles need accurate 3D perception with a decent frame rate and high angular resolution to detect obstacles reliably and avoid collisions. We developed a low-cost scanning multichannel light detection and ranging sensor architecture allowing scalable frame rates by adjusting the number of laser and detector pairs. Scanning is achieved by a pair of micro-electro-mechanical system (MEMS) mirrors. A control pattern for the MEMS mirrors to maximize the frame rate is presented. A built prototype based on the proposed architecture achieves a frame rate of 11.5 Hz, a field of view of 70∘×30∘, and an angular resolution of 0.4°. The distance resolution is 6 cm. Reliable single-shot detection for low-reflective objects up to 19 m indoors and 11 m under direct sunlight exposure is achieved. A performance assessment based on the presented measurement system for recently available vertical-cavity surface-emitting laser arrays with power densities up to 1k W/m m 2 shows promising improvement potential.

This work introduces a process to develop a tool-independent, high-fidelity, ray tracing-based light detection and ranging (LiDAR) model. This virtual LiDAR sensor includes accurate modeling of the scan pattern and a complete signal processing toolchain of a LiDAR sensor. It is developed as a functional mock-up unit (FMU) by using the standardized open simulation interface (OSI) 3.0.2, and functional mock-up interface (FMI) 2.0. Subsequently, it was integrated into two commercial software virtual environment frameworks to demonstrate its exchangeability. Furthermore, the accuracy of the LiDAR sensor model is validated by comparing the simulation and real measurement data on the time domain and on the point cloud level. The validation results show that the mean absolute percentage error (MAPE) of simulated and measured time domain signal amplitude is 1.7%. In addition, the MAPE of the number of points Npoints and mean intensity Imean values received from the virtual and real targets are 8.5% and 9.3%, respectively. To the author's knowledge, these are the smallest errors reported for the number of received points Npoints and mean intensity Imean values up until now. Moreover, the distance error derror is below the range accuracy of the actual LiDAR sensor, which is 2 cm for this use case. In addition, the proving ground measurement results are compared with the state-of-the-art LiDAR model provided by commercial software and the proposed LiDAR model to measure the presented model fidelity. The results show that the complete signal processing steps and imperfections of real LiDAR sensors need to be considered in the virtual LiDAR to obtain simulation results close to the actual sensor. Such considerable imperfections are optical losses, inherent detector effects, effects generated by the electrical amplification, and noise produced by the sunlight.

LiDAR sensors are a key technology for enabling safe autonomous cars. For highway applications, such systems must have a long range, and the covered field of view (FoV) of >45° must be scanned with resolutions higher than 0.1°. These specifications can be met by modern MEMS scanners, which are chosen for their robustness and scalability. For the automotive market, these sensors, and especially the scanners within, must be tested to the highest standards. We propose a novel measurement setup for characterizing and validating these kinds of scanners based on a position-sensitive detector (PSD) by imaging a deflected laser beam from a diffuser screen onto the PSD. A so-called ray trace shifting technique (RTST) was used to minimize manual calibration effort, to reduce external mounting errors, and to enable dynamical one-shot measurements of the scanner’s steering angle over large FoVs. This paper describes the overall setup and the calibration method according to a standard camera calibration. We further show the setup’s capabilities by validating it with a statically set rotating stage and a dynamically oscillating MEMS scanner. The setup was found to be capable of measuring LiDAR MEMS scanners with a maximum FoV of 47° dynamically, with an uncertainty of less than 1%.

Due to their high light throughput, static single-mirror Fourier transform spectrometers (sSMFTS) are well suited for spectral analysis in the mid-infrared range, and at the same time feature a more robust and compact design than conventional scanning instruments. One major drawback, however, is the comparably low spectral resolution, which is mainly limited by the number of detector pixels. Therefore, in this article, we propose a cost-effective design that almost doubles the spectral resolution of an sSMFTS by integrating a stepped mirror in one of the interferometer arms. The calibration process is described and a proof of principle is given by measuring a CO2 laser. The design works in a spectral range from about 2800 cm−1 to 600 cm−1 at a spectral resolution of 7 cm−1 and offers the potential to improve resolution even further.

In this contribution we introduce a compact version of a broadband static Fourier transform spectrometer (bs- FTS) for the mid-infrared spectral range. The bsFTS covers a spectral range from about 4.5 µm to 14 µm, respectively 2220 cm−1 to 700 cm−1 at a spectral resolution of 8 cm−1. As, in contrast to scanning Fourier trans- form spectrometers, the interferogram is modulated not over time but in the spatial domain, the measurement speed is only limited by the detector. This allows for infrared spectroscopy at 25 Hz to 200 Hz using uncooled microbolometer arrays. Besides liquid measurements in attenuated total reflection (ATR), demonstrating the accuracy and linearity of the bsFTS, we show time-resolved analyses of 1,1,1,2-Tetrafluoroethane (R134a) and carbon monoxide test gases to prove the suitability of the system for high-speed spectroscopy.

In this paper, we present a static imaging Fourier transform spectrometer (sIFTS) for the mid-infrared spectral range. The system employs imaging optics, a single-mirror interferometer, and an uncooled broadband microbolometer array. As the hyperspectral data cube is acquired using a windowing method, a comparatively high light throughput in a spectral range from 2600 cm(-1) to 800 cm(-1), respectively 3.8 mu m to 13 mu m is achieved. The spectral resolution is 12 cm(-1), and the spatial resolution amounts to 16 lp/mm, corresponding to a resolution of 62.5 mu m at a design wavelength of 10.6 mu m. As the employed spectrometer, in contrast to scanning Fourier transform infrared (FTIR) spectrometers, contains no moving parts, the measurement rate is mainly limited by the detector read-out and is currently 25 Hz. After an evaluation of the spatial resolution by both simulations and experimental results, the spatially resolved transmission spectra of a sample containing different polymer films are recorded and compared to a laboratory FTIR spectrometer. Thereby, the acquired spectra show good agreement. As the system combines both a spectrometer with low internal light loss and a windowing technique allowing high etendue, the presented hyperspectral imager shows significant potential especially for the mid-infrared. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

In this contribution we present a broadband static Fourier transform spectrometer (bsFTS) based on a single-mirror interferometer containing only off-the-shelf optical components and an uncooled microbolometer detector array. The system uses concave mirrors instead of lenses and therefore covers a wide spectral range from 3:6 mu m to 17 mu m at a spectral resolution of 12 cm. Furthermore, dispersion effects can be minimized and the system can thus be designed with increased temperature stability. We demonstrate the optical and mechanical design of the current laboratory prototype and compare the instrument to a scanning Fourier transform infrared (FTIR) spectrometer. Additionally, we present a technique for simultaneously acquiring the sample spectrum and the background spectrum. Thereby, a variation of the background over time can be compensated continuously and hence the bsFTS presented in this contribution offers significant potential with regard to long-term stability.

Zusammenfassung In diesem Beitrag wird die Kalibration eines kompakten optischen Sensors für die Online-Ölzustandsüberwachung in Verbrennungsmotoren vorgestellt. Der Sensor basiert auf dem Prinzip der nicht-dispersiven Infrarotspektroskopie (NDIR-Spektroskopie) und misst die Absorption in sieben verschiedenen Spektralbereichen. Mit Hilfe multivariater Regressionsmethoden werden lineare Modelle erstellt, die den Zusammenhang zwischen infraroter Absorption und wichtigen Ölzustandsparametern, wie beispielsweise Wassergehalt, Oxidation, Sulfation und Basenzahl, beschreiben. Die Validierung der Modelle anhand eines Datensatzes von Gebrauchtölproben aus unterschiedlichen Anwendungen zeigt eine gute Übereinstimmung mit Referenzwerten aus einer Laboranalyse. Durch den Einsatz von Kalibrationstransferverfahren können die erstellten Regressionsmodelle auf einem zweiten Sensor gleicher Bauart angewendet werden, wobei sich die Prädiktionsgüte nur geringfügig verschlechtert.

Combining reflectometry and hyperspectral imaging allows mapping of thin film thickness. Therefore, layer thickness is calculated by comparing a dataset of simulated spectra with the measured data. Utilizing the maximum frame rate of the hyperspectral imager, the pixel wise spectra comparing procedure cannot be performed using a standard computer due to the processing load. In this work, a method using neural networks for calculating layer thickness is presented. By the use of the nonlinear equation as result of a trained neural network, thickness data can be determined with a measurement rate matching the maximum frame rate of the hyperspectral imager.

Fourier transform spectroscopy has become a standard method for spectral analysis of infrared light. With this method, an interferogram is created by two beam interference which is subsequently Fourier-transformed. Most Fourier transform spectrometers used today provide the interferogram in the temporal domain. In contrast, static Fourier transform spectrometers generate interferograms in the spatial domain. One example of this type of spectrometer is the static single-mirror Fourier transform spectrometer which offers a high etendue in combination with a simple, miniaturized optics design. As no moving parts are required, it also features a high vibration resistance and high measurement rates. However, it is susceptible to temperature variations. In this paper, we therefore discuss the main sources for temperature-induced errors in static single-mirror Fourier transform spectrometers: changes in the refractive index of the optical components used, variations of the detector sensitivity, and thermal expansion of the housing. As these errors manifest themselves in temperature-dependent wavenumber shifts and intensity shifts, they prevent static single-mirror Fourier transform spectrometers from delivering long-term stable spectra. To eliminate these shifts, we additionally present a work concept for the thermal stabilization of the spectrometer. With this stabilization, static single-mirror Fourier transform spectrometers are made suitable for infrared process spectroscopy under harsh thermal environmental conditions. As the static single-mirror Fourier transform spectrometer uses the so-called source-doubling principle, many of the mentioned findings are transferable to other designs of static Fourier transform spectrometers based on the same principle.

In this article, the design and application of an optical sensor for fluid monitoring based on two tunable Fabry-Perot filters is presented. The sensor enables fluid transmission measurements in the spectral ranges from 1818 cm(-1) to 1250 cm(-1) and from 1250 cm(-1) to 952 cm(-1) at wavenumber-dependent resolutions between approximately 20 cm(-1) and 33 cm(-1). A novel method is proposed to ensure a correct wavenumber representation of the obtained spectra, correcting for an oblique light path in the optical system. The sensor shows high linearity and a low noise level for absorbance measurements. As an example for a fluid monitoring application, the transmission spectra of deteriorated automotive engine oil samples were measured and compared to spectra obtained with a Fourier-transform infrared spectrometer. We used partial least squares (PLS) regression to construct calibration models for the prediction of significant oil condition parameters, like oxidation, sulfation, water content and viscosity, from the obtained spectra. The values predicted from the absorption spectra show high correlation with reference values of the oil condition parameters determined in a laboratory according to appropriate standards. These results indicate that the sensor can be a useful supplementary tool for fast and cost-effective engine oil condition monitoring. (C) 2017 Elsevier B.V. All rights reserved.

Online monitoring of gases in industrial processes is an ambitious task due to adverse conditions such as mechanical vibrations and temperature fluctuations. Whereas conventional Fourier transform infrared (FTIR) spectrometers use rather complex optical and mechanical designs to ensure stable operation, static FTIR spectrometers do not require moving parts and thus offer inherent stability at comparatively low costs. Therefore, we present a novel, compact gas measurement system using a static single-mirror Fourier transform spectrometer (sSMFTS). The system works in the mid-infrared range from 650 cm - 1 to 1250 cm - 1 and can be operated with a customized White cell, yielding optical path lengths of up to 120 cm for highly sensitive quantification of gas concentrations. To validate the system, we measure different concentrations of 1,1,1,2-Tetrafluoroethane (R134a) and perform a PLS regression analysis of the acquired infrared spectra. Thereby, the measured absorption spectra show good agreement with reference data. Since the system additionally permits measurement rates of up to 200 Hz and high signal-to-noise ratios, an application in process analysis appears promising.

The condition of lubricating oil used in automotive and industrial gearboxes must be controlled in order to guarantee optimum performance and prevent damage to machinery parts. In normal practice, this is done by regular oil change intervals and routine laboratory analysis, both of which involve considerable operating costs. In this paper, we present a compact and robust optical sensor that can be installed in the lubrication circuit to provide quasi-continuous information about the condition of the oil. The measuring principle is based on non-dispersive infrared spectroscopy. The implemented sensor setup consists of an optical measurement cell, two thin-film infrared emitters, and two four-channel pyroelectric detectors equipped with optical bandpass filters. We present a method based on multivariate partial least squares regression to select appropriate optical bandpass filters for monitoring the oxidation, water content, and acid number of the oil. We perform a ray tracing analysis to analyze and correct the influence of the light path in the optical setup on the optical parameters of the bandpass filters. The measurement values acquired with the sensor for three different gearbox oil types show high correlation with laboratory reference data for the oxidation, water content, and acid number. The presented sensor can thus be a useful supplementary tool for the online condition monitoring of lubricants when integrated into a gearbox oil circuit.

Zusammenfassung Statische Einzelspiegel-Fourier-Transformations-Spektrometer ermöglichen Spektroskopie mit hohen Messraten im mittleren infraroten Spektralbereich, da sie geringe interne Lichtverluste aufweisen und gleichzeitig auf bewegliche Teile vollständig verzichten. In dieser Arbeit wird ein solches Spektrometer vorgestellt, optimiert und auf seine maximale Messrate hin untersucht. Ausgestattet mit einem ungekühlten Mikrobolometerarray als Detektor erlaubt es Bildwiederholraten bis 200 Hz bei einer Auflösung von 7.1 cm –1 im Spektralbereich zwischen 1250 cm –1 und 650 cm –1. Durch die hohe thermische Zeitkonstante des Mikrobolometerarrays ist für quantitative spektrale Messungen die Messrate des Gesamtsystems auf 15 Hz zu reduzieren.