Real-time Simulation of  Physical Multi-sensor Setups

Ahn, Natalya; Höfer, Andreas; Herrmann, Martin; Donn, Christian

doi:10.1007/s38314-020-0207-1

Cited by 2 publications

(2 citation statements)

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“…Automotive perception sensor models can be divided into three categories; ideal, phenomenological, and physical models depending on their modeling approach and covered effects [ 20 ].…”

Section: State Of the Artmentioning

confidence: 99%

“…CarMaker provides the real-time capable ray tracing-based LiDAR model known as LiDAR raw signal interface (LiDAR RSI). The LiDAR RSI regards propagation losses, object geometry, material surface properties, and incidence angle of ray for intensity calculation of point clouds [ 20 ]. DYNA4 from Vector offers a LiDAR model which uses a ray casting technique that outputs raw data intensity based on physical effects, the material surface reflectivity, and ray angle of incidence [ 38 ].…”

Section: State Of the Artmentioning

confidence: 99%

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Development of High-Fidelity Automotive LiDAR Sensor Model with Standardized Interfaces

Haider

Pigniczki

Köhler³

et al. 2022

Sensors

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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.

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“…Automotive perception sensor models can be divided into three categories; ideal, phenomenological, and physical models depending on their modeling approach and covered effects [ 20 ].…”

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Development of High-Fidelity Automotive LiDAR Sensor Model with Standardized Interfaces

Haider

Pigniczki

Köhler³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

Assessing Autonomous Emergency Braking: A Robust Approach Using Phenomenological Sensor Models

Maschke,

Preu,

Plenter

et al. 2024

Vehicles

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The assessment of driver assistance system effectiveness in crash scenarios can be conducted through simulations. This approach allows for system improvements and contributes to overall road safety. However, it is important to recognize that simulations are an approximation of reality. Specifically, simulation models can significantly impact the results. In our work, we focus on sensor models that provide environmental information to driver assistance systems. Real-world sensor data differ from idealized simulation data due to measurement errors and environmental influences. We evaluate various sensor models for their applicability in simulating effectiveness assessments. Our research is implemented using the open-source platform openPASS. We investigate the impact of inaccurate sensor data on the effectiveness of an autonomous emergency braking system based on longitudinal traffic crashes from the German In-Depth Accident Study database. To achieve this, we compare stochastic phenomenological models with ideal sensor models. In the evaluation of the modeled autonomous emergency braking, we observed a major influence on the effectiveness due to the less accurate, more realistic sensor data. This evaluation approach enables early optimization of the considered driver assistance system’s robustness against inaccuracies.

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Real-time Simulation of Physical Multi-sensor Setups

Cited by 2 publications

References 0 publications

Development of High-Fidelity Automotive LiDAR Sensor Model with Standardized Interfaces

Development of High-Fidelity Automotive LiDAR Sensor Model with Standardized Interfaces

Assessing Autonomous Emergency Braking: A Robust Approach Using Phenomenological Sensor Models

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