Method and Applications of Lidar Modeling for Virtual Testing of Intelligent Vehicles

Zhao, Jian; Li, Yaxin; Zhu, Bing; Deng, Weiwen; Sun, Bohua

doi:10.1109/tits.2020.2978438

Cited by 31 publications

(27 citation statements)

References 21 publications

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“…LiDAR sensor is widely used in unmanned vehicles with high detection accuracy and resolution and provides rich 3D data description as well as information on the reflection intensity of the detected obstacle surface at the same time [17]. The current LiDAR is mainly divided into mechanical rotating LiDAR and solid-state LiDAR.…”

Section: Lidarmentioning

confidence: 99%

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

et al. 2021

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Environmental perception technology is the basis and premise of intelligent vehicle decision control of intelligent vehicles, a crucial link of intelligent vehicles to realize intelligence, and also the basic guarantee of its safety and intelligence. The accuracy and robustness of the perception algorithm will directly affect or even determine the realization of the upper function of intelligent vehicles. The wrong environmental perception will affect the control of the vehicle, thus causing safety risks. This paper discusses the intelligent vehicle perception technology and introduces the development status and control strategies of several important sensors such as machine vision, laser radar, and millimeter-wave radar. Target detection, target recognition, and multisensor fusion are analyzed in the optimized part of sensor results. The functions of the intelligent vehicle assistance system which has been applied to the ground at present are described, and the lane detection, adaptive cruise control (ACC), and autonomous emergency braking (AEB) are analyzed. Finally, the paper looks forward to the research direction of sense-based intelligent vehicle perception technology, which will play an important role in guiding the development of intelligent vehicles and accelerate the landing process of intelligent vehicles.

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Section: Lidarmentioning

confidence: 99%

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

et al. 2021

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“…In addition, the model contains beam divergence and power loss due to rain, fog, snow, and haze. Zhao et al [ 29 ] extend the work of [ 28 ]. Their model takes the probability of false alarms due to the backscattering from water droplets.…”

Section: State Of the Artmentioning

confidence: 97%

“…The model’s output can be either an object list or raw data. A LiDAR model consisting of geometrical and physical submodules is presented in [ 28 , 29 ]. The input of the geometrical model is the object list, including the occlusion, FoV, and beam divergence.…”

Section: State Of the Artmentioning

confidence: 99%

“…Moreover, the suggested model includes beam divergence, material reflection properties, detection threshold, noise effects, and atmospheric attenuation. Li et al [ 28 ] developed a physical sensor model that requires ray tracing data as an input. In addition, the model contains beam divergence and power loss due to rain, fog, snow, and haze.…”

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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“…and rain that confuse the LiDAR sensors to detect false targets, and additional interference effects when using multiple LiDARS simultaneously. Zhao et al [31] developed an automotive simulation package that includes geometric modelling, attenuation using the laser radar equation, and a probabilistic model for reflection from homogeneously distributed raindrops. Outdoor validation tests were performed, but because these were uncontrolled these provided only intuitive comparisons with the simulated data.…”

Section: A Attenuation and Scattering Of The Signalmentioning

confidence: 99%

Full Waveform LiDAR for Adverse Weather Conditions

Wallace

Halimi

Buller

2020

IEEE Trans. Veh. Technol.

100

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We present and discuss the case for full waveform pixel and image acquisition and processing to enable LiDAR sensors to penetrate and reconstruct 3D surface maps through obscuring media. To that end, we review work on signal propagation, on scanning and arrayed sensors, on signal processing strategies for independent pixels and employing spatial context, on reducing complexity and accelerating processing by sensor design, algorithmic changes, compressed sensing, and parallel processing. We report several experimental studies on LiDAR imaging through complex media, and how these can inform the automotive LiDAR scenario. We conclude with a discussion of future development and potential for full waveform LiDAR (FWL).

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Method and Applications of Lidar Modeling for Virtual Testing of Intelligent Vehicles

Cited by 31 publications

References 21 publications

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

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

Full Waveform LiDAR for Adverse Weather Conditions

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