Martin Jakobi

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.

This article presents a mathematical model of the Michelson interferometer (MI)-based membrane-less optical microphone (MeoM)–photoacoustic spectroscopy (MeoM–PAS) method, which is also referred to as MI-based photoacoustic interferometry (PAI), for gas-sensing applications in complex and adverse environments, as it offers a completely static measurement system and the separation of a photoacoustic (PA) gas cell from the measuring system. It also investigates the dependence of this method on the fundamental parameters of a cubical PA gas cell using axial PA signals. The results indicate that the phase of the method is a sine function of the distance between the two light beams and a power exponent of the cell length, the cell height, and the distance between the excitation source and the nearest light beam, under the condition that the PA gas cell is resonant and that the excitation source is at the position of the peak or valley of the PA signals. It is at its maximum when the distance between the two light beams is approximately half the wavelength of the PA signals under the same conditions. In addition, the dependence of a PA gas cell using non-axial PA signals is described under the conditions that the PA gas cell is resonant, which is consistent with the changing aforementioned parameters for the distance between the two light beams, the cell length and height, and the distance between the excitation source and the nearest light beam. Furthermore, the selection of five common materials (aluminum, brass, glass, quartz, and stainless steel) for the PA gas cell is discussed under the influence of temperature fluctuations outside the PA gas cell, noise inside and outside the PA gas cell, as well as thermal and viscous losses inside the PA gas cell. The results indicate that quartz and stainless steel are promising options. Finally, the parameters related to the sensitivity enhancement of the method are analyzed using mathematical models, where the sensitivity of the method can be theoretically enhanced by reducing the dimensions of the PA gas cell.

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.

Regenerated fiber Bragg gratings (RFBGs) have been created in large mode area (LMA) fibers, and their key properties for their application as high-temperature sensors were experimentally characterized. UV written Type-I seed gratings in standard single-mode fibers (SMF-28) and in low numerical aperture LMA fibers with 125 μm and 250 μm cladding diameters were regenerated, and their wavelength drift characteristics at high temperatures, their temperature sensitivities, and their mechanical strengths were tested and compared with each other. We found that low dopant concentrations in the fiber cores led to lower wavelength drift rates during and after regeneration. A temperature calibration from room temperature to 800 $^\circ$ C showed that LMA fibers have generally similar but slightly lower temperature sensitivities than SMF-28 fibers. The tensile strength tests showed that LMA fibers with 250 μm cladding diameter have similar tensile fracture stresses as SMF-28 fibers but four times larger tensile force tolerances because of their twofold fiber diameter. Our research shows that using LMA fibers for RFBGs offers significant advantages for high-temperature sensing than their counterparts in standard fibers, which are also applicable to other silica fiber-based high-temperature sensors.

Modal interference in a multimode fiber (MMF) has been utilized to develop simple sensors for various physical parameters. Herein, first, we investigate the dependences of the core diameter and the numerical aperture (NA) of the MMF on the performance of multimode-interference-based strain and temperature sensing. We find that a larger core diameter leads to higher temperature sensitivity but lower strain sensitivity (absolute value) and that higher NA does not influence the temperature sensitivity but results in higher strain sensitivity (absolute value). Subsequently, using the obtained low strain sensitivity, we demonstrate strain-insensitive temperature sensing with a sensitivity of approximately 6.1 pm °C⁻¹.

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%.