Full-Waveform Modeling for Time-of-Flight Measurements based on Arrival Time of Photons

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.

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“…The power signal must be sampled with a

time interval to accurately model the optics at the photon level [ 39 ]. The sampled power equation takes the form of:

with

.…”

Section: Modeling Of the Rain And Fog Effect In The Virtual Lidar Sensormentioning

confidence: 99%

A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors

Haider

Pigniczki

Koyama

et al. 2023

Sensors

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“…The task of the detector module is to capture these photons’ arrivals and convert them into an electrical current signal

. In the proposed LiDAR model, we have implemented silicon photomultipliers (SiPM) as a detector [ 47 ]. Still, it can also support avalanche photodiode (APD) and single-photon avalanche diode (SPAD) detector models.…”

Section: Lidar Sensor Modelmentioning

confidence: 99%

“…It is possible to model the optics at the photon level based on the power equation to make the simulation more accurate. For this approach, the power signal must be sampled with time interval of

[ 47 ]. Then, the sampled power equation takes the form of

with

.…”

Section: Lidar Sensor Modelmentioning

confidence: 99%

“…We have implemented the SiPM detector module that provides an output current proportional to the number of photons [ 47 ]. In contrast to the SPAD, the SiPM detector yields better multi-photon detection sensitivity, photon number resolution, and extended dynamic range [ 54 , 55 ].…”

Section: Lidar Sensor Modelmentioning

confidence: 99%

“…We use the small-signal transfer function

of the analog circuit model to obtain the voltage signal

where

is the operating voltage of the circuit model,

is the inverse discrete Fourier transform (IDFT),

shows the discrete Fourier transform (DFT), and

denotes the small-signal voltage of the circuit model [ 47 ]. The output voltages of the circuit module are given in Figure 10 .…”

Section: Lidar Sensor Modelmentioning

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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Low-cost scanning LIDAR architecture with a scalable frame rate for autonomous vehicles

Fink

Schardt²,

Baier

et al. 2023

Appl. Opt.

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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 1 k W / m m 2 shows promising improvement potential.

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Full-Waveform Modeling for Time-of-Flight Measurements based on Arrival Time of Photons

Cited by 3 publications

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A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors

A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors

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

Low-cost scanning LIDAR architecture with a scalable frame rate for autonomous vehicles

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