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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.