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- LiDAR sensor; rain; fog; sunlight; advanced driver-assistance system; backscattering; Mie theory; open simulation interface; functional mock-up interface; functional mock-up unit (1)
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Die Entwicklung, Erprobung und Validierung von Fahrerassistenzsystemen und automatisierten Fahrfunktionen ist im realen Fahrversuch aufgrund mangelnder Skalierbarkeit nur eingeschränkt möglich. IPG Automotive und die Hochschule Kempten beschreiben eine effziente Simulations-Toolchain, die eine nahtlose Integration und Austauschbarkeit verschiedener Sensormodelle und Systemkomponenten ermöglicht.
Developing, testing and validating advanced driver assistance systems and automated driving functions can only be realized to a limited extent in real-world test drives due to a lack of scalability. IPG Automotive and the Kempten University describe an efficient simulation toolchain that enables the seamless integration and exchange of different sensor models and system components.
Electronics Reliability Prediction by using Physics of Failures SHERLOCK Automated Design Analysis
(2018)
In this modern era as the electronics technology is progressing systems and products are becoming more complex. Rapid progress in electronics technology increases its complexity too. These rapid changes in electronics technology leads to new failure modes and standard reliability tools have to tackle with all these challenges. New technologies required the adapted approaches that should be cost effective in order to make sure product will be meet its desired reliability goals. Since so many years lot of approaches have been used in order to predict the reliability of Avionics and on ground electronics but physics of failure (POF) is one the most reliable approach for this purpose. This paper will provide insight to a process used to predict reliability of avionics electronics by using Sherlock ™ Automated Design.
Phase noise (PN) is one of the most significant impairments adversely affecting the detection performance of frequency-modulated continuous wave (FMCW) radar systems. Due to the rapid advance of advanced driver assistance systems (ADAS), virtual testing and the evaluation of highlyautomated driving (HAD) functions became indispensable. In this work, the impact of PN on the performance of automotive radar sensors is demonstrated on HAD functions in a virtual driving simulator. Therefore, a PN model initially developed for static objects is applied to dynamic scenarios including moving objects. By implementing a real world scenario in the virtual environment the influence of PN on the detection performance of the radar sensor is demonstrated. The virtual test scenario is implemented using the CarMaker test driving software, which is commonly accepted as an accurate and reliable tool by the automotive industry. The radar sensor model including PN is implemented as a functional mock-up unit (FMU) using the standardized functional mock-up interface (FMI) 2.0 and the open simulation interface (OSI) 3.0.0. Finally, the radar FMU model simulations are compared with hardware measurements.
Fullerene solar cells are becoming a feasible choice due to advanced developments in donor materials and improved fabrication techniques of devices. Recently, sufficient optimization and improvements in processing techniques like incorporation of solvent vapor annealing (SVA) with additives in solvents has become a major reason for prominent improvements in the performance of organic solar cell-based devices . On the other hand, the challenge of reduced open circuit voltage (Voc) remains. This study presents an approach for significant performance improvement of overall device based on organic small molecular solar cells (SMSCs) by following a two step technique that comprises thermal annealing (TA) and SVA (abbreviated as SVA+TA). In case of exclusive use of SVA, reduction in Voc can be eliminated in an effective way. The characteristics of charge carriers can be determined by the measurement of transient photo-voltage (TPV) and transient photo-current (TPC) that determines the scope for improvement in the performance of device by two step annealing. The recovery of reduced Voc is linked with the necessary change in the dynamics of charge that lead to increased overall performance of device. Moreover, SVA and TA complement each other; therefore, two step annealing technique is an appropriate way to simultaneously improve the parameters such as Voc, fill factor (FF), short circuit current density (Jsc) and PCE of small molecular solar cells.
In this paper, we derive intermediate frequency (IF) level analytical formulation of radio frequency (RF) group delay for automotive frequency-modulated continuous-wave (FMCW) radar waveform under quasi-static approximation. To the best of our knowledge, this paper is the first to develop and simulate an IF-level analytical form ulation of RF group delay, including random and deterministic variation for the FMCW radar waveform. Theoretical limitation for the tolerable RF group delay can be derived based on the proposed model. We demonstrated the impact of RF group delay on the FMCW radar sensor's range spectrum in dynamic virtual traffic scenarios. The proposed model is integrated into a virtual FMCW radar sensor model implemented as a functional mock-up unit (FMU) using the standardized interfaces functional mock-up interface (FMI) 2 .0 and the open simulation interface (OSI) 3. 0. 0. A virtual test scenario is implemented in an industry-standard simulation tool, CarMaker, to demonstrate the effect.
The presentation shows the development of a physical LiDAR/RADAR sensor behavioral models using standardized interfaces Open Simulation Interface (OSI) and Functional Mockup Interface (FMI). Furthermore, different metrics to validate the similarity between the LiDAR model and real measurement on the point cloud level are discussed.
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.
A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
(2023)
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.
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.
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 paper provides an approach for controlling the level of risk when operating highly automated transportation systems like cars, trains and similar. Such systems replace human perception and decision-making by employing highly sophisticated solutions based on electronics, IT, and AI. Such systems have demonstrated the potential for building highly automated vehicles, but, as of today, encounter challenges in correctly understanding the extremely complex open contexts, into which such vehicles could be deployed. Its key focus is on bounding the risks stemming from uncertainty in the perception of the environment.
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.
Der Beitrag zeigt die Modellierung, Simulation und den Test des Einflusses von Regen und Nebel auf Messungen mit LiDAR- und Radar-Sensoren. Zunächst wurden relevante Kriterien zur Modellierung hergeleitet und erläutert. Die Anwendung der Theorie der Mie-Streuung und Rayleigh-Streuung führte zu einer Entwicklung von LiDAR und Radar Sensor Simulationsmodellen. Diese Modelle wurden durch Tests in Regen-Testananlagen validiert. Hierbei liefern Key Performance Parameters (KPIs) quantitative Ergebnisse wie Signal Dämpfung, Signal-Rauschverhältnis (SNR), Detektionsrate, Fehlerrate, Entfernungsfehler.