Influence of sensor blockage on automotive LiDAR systems

Trierweiler, Manuel; Caldelas, P.; Groninger, Gabriel; Peterseim, Tobias; Neumann, Cornelius

doi:10.1109/sensors43011.2019.8956792

Cited by 26 publications

(19 citation statements)

References 7 publications

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“…Trierweiler et al [57] investigated the degradation of LiDAR efficiency due to the pollution of its cover glass. In particular, it has been determined that the maximum range of detection of objects can be reduced up to 75%.…”

Section: The Influence Of Harsh Weather and The Environment On The Lidarsmentioning

confidence: 99%

A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in “Smart Cities” under Various External Influences

Meshcheryakov

Iskhakov

Mamchenko

et al. 2022

Sensors

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The paper proposes an approach to assessing the allowed signal-to-noise ratio (SNR) for light detection and ranging (LiDAR) of unmanned autonomous vehicles based on the predetermined probability of false alarms under various intentional and unintentional influencing factors. The focus of this study is on the relevant issue of the safe use of LiDAR data and measurement systems within the “smart city” infrastructure. The research team analyzed and systematized various external impacts on the LiDAR systems, as well as the state-of-the-art approaches to improving their security and resilience. It has been established that the current works on the analysis of external influences on the LiDARs and methods for their mitigation focus mainly on physical (hardware) approaches (proposing most often other types of modulation and optical signal frequencies), and less often software approaches, through the use of additional anomaly detection techniques and data integrity verification systems, as well as improving the efficiency of data filtering in the cloud point. In addition, the sources analyzed in this paper do not offer methodological support for the design of the LiDAR in the very early stages of their creation, taking into account a priori assessment of the allowed SNR threshold and probability of detecting a reflected pulse and the requirements to minimize the probability of “missing” an object when scanning with no a priori assessments of the detection probability characteristics of the LiDAR. The authors propose a synthetic approach as a mathematical tool for designing a resilient LiDAR system. The approach is based on the physics of infrared radiation, the Bayesian theory, and the Neyman–Pearson criterion. It features the use of a predetermined threshold for false alarms, the probability of interference in the analytics, and the characteristics of the LiDAR’s receivers. The result is the analytical solution to the problem of calculating the allowed SNR while stabilizing the level of “false alarms” in terms of background noise caused by a given type of interference. The work presents modelling results for the “false alarm” probability values depending on the selected optimality criterion. The efficiency of the proposed approach has been proven by the simulation results of the received optical power of the LiDAR’s signal based on the calculated SNR threshold and noise values.

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Section: The Influence Of Harsh Weather and The Environment On The Lidarsmentioning

confidence: 99%

A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in “Smart Cities” under Various External Influences

Meshcheryakov

Iskhakov

Mamchenko

et al. 2022

Sensors

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“…The complexity and variability of the real environment introduce varying degrees of uncertainty into sensor measurements. The impact of weather and dust on the accuracy of LiDAR systems has been studied by major car manufacturers (e.g., Robert Bosch [33], BMW [30], Audi [31]). However, the effect of dirt on the performance of 3D cameras has been a gap so far.…”

Section: A Motivationmentioning

confidence: 99%

Evaluation and Compensation of the Effect of Dirt on Time-of-Flight 3D Imaging Systems

Liu

Conde

2022

IEEE Sensors J.

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In the past few years, 3D imaging technology has received a lot of attention. Time-of-Flight (ToF) cameras based on the indirect ToF principle calculate depth information by measuring the phase shift between the emitted periodic signal and the reflected signal. When the illumination from the modulated light source reaches the same pixel through multiple paths, multipath interference will occur. Acquisitions at multiple modulation frequencies are then required to separate the interfering paths. Parametric spectral estimation methods, such as Prony's method and its robust variants, have shown success in resolving multiple paths from multi-frequency measurements. With the widespread usage of ToF cameras in various fields, the operating environment of the camera should be taken into account in order to obtain accurate 3D images.In this work, we aim to study the effect of dirt on 3D imaging systems quantitatively. To this end, ten sheets with different transmittances, between 50% and 100% are used. To the best of our knowledge, for the first time we provide standardized methods for evaluating the effect of dirt on protective covers in the depth estimation of "flash lidar". The accuracy of depth measurements obtained by multi-frequency multipath recovery in the absence of dust is up to 99%. However, when the light transmittance of the protective cover is less than 90%, the measurement result is already unreliable. The experiments have shown that the depth measurements obtained using the multi-frequency estimation method are more accurate in the presence of dust than the single-frequency estimation method.

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“…The more particles there are between the measuring device and the target object or on the optics of the measuring device, the more the signal is reduced on its way. The effect of dust on the sensor optics is investigated experimentally for example in [ 59 ]. A correlation between the number and type of dust particles in the air and the influence on the measurement is presented in [ 60 ].…”

Section: Fundamentals Of Depth-sensingmentioning

confidence: 99%

BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data

Reitmann

Neumann

Jung

2021

Sensors

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Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the training data generation process for a wide range of domains, we have developed the BLAINDER add-on package for the open-source 3D modeling software Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniques Light Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within the BLAINDER add-on, different depth sensors can be loaded from presets, customized sensors can be implemented and different environmental conditions (e.g., influence of rain, dust) can be simulated. The semantically labeled data can be exported to various 2D and 3D formats and are thus optimized for different ML applications and visualizations. In addition, semantically labeled images can be exported using the rendering functionalities of Blender.

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Influence of sensor blockage on automotive LiDAR systems

Cited by 26 publications

References 7 publications

A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in “Smart Cities” under Various External Influences

A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in “Smart Cities” under Various External Influences

Evaluation and Compensation of the Effect of Dirt on Time-of-Flight 3D Imaging Systems

BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data

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