LiDAR Performance Review in Arctic Conditions

Jokela, Maria; Pyykönen, Pasi; Kutila, Matti; Kauvo, Kimmo

doi:10.1109/iccp48234.2019.8959554

Cited by 10 publications

(2 citation statements)

References 4 publications

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“…Despite several tests with the Ibeo LUX LiDAR, the overall study focuses on detecting the presence of adverse weather conditions (fog, snow, or rain) and assessing their impact on the LIDAR, and the influence of fog and snow on the overall efficiency of the detection of obstacles of the UV when moving was not considered. Jokela et al analyzed the behavior of the LiDARs in arctic weather conditions [53]. The study included an assessment of the influence of high air humidity, wet road surface, low temperatures and snowstorms on Cepton HR80T, Ibeo Lux, Velodyne Puck VLP-16, and Robosense RS-LiDAR-32 LiDARs.…”

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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“…Waymo's fully self-driving technology, along with the LiDAR system developed by themselves, has driven over 20 million miles on real-world roads since 2009 [24]. Several LiDAR sensors are implemented and tested on road with different weather conditions in [25]. LiDAR's rich selection of components generate different data types.…”

Section: Introductionmentioning

confidence: 99%

Data Processing Approaches on SPAD-Based d-TOF LiDAR Systems: A Review

2021

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With the rise of advanced driver assistance systems (ADAS), range sensors and their data processing methods are becoming more and more important. Light detection and ranging (LiDAR) sensors are attracting attention due to their unique advantages in terms of radial distance resolution and detection range. However, the study of LiDAR data processing is usually divorced from the LiDAR sensor measurement process itself. This leads to critical measurement information being overlooked. This paper seeks a breakthrough to improve the performance of singlephoton-avalanche-diode-based direct time-of-flight LiDAR systems by reviewing the data processing stages and corresponding processing approaches for LiDAR measurements, starting from photon incidence and ending with high-level feature recognition. Firstly, we propose a LiDAR system model based on data generation and transfer. The data forms in such a LiDAR system are mainly classified into timestamps, time-correlated histograms, point cloud data, and high-level properties. Subsequently, data processing methods applied to each of these data forms are analyzed. A number of hardware solutions closely related to data transmission and control are also included in the discussion. The principles, limitations, and challenges of these methods are discussed in detail and the criteria for evaluation of time-correlated histograms in ADAS are proposed. Finally, the research gaps in data processing are summarized, and future directions for research development are presented.

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Lidar De-snowing Method with Density and Intensity Fusion

Pan,

Wang

2024

Lecture Notes in Electrical Engineering

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LiDAR Performance Review in Arctic Conditions

Cited by 10 publications

References 4 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

Data Processing Approaches on SPAD-Based d-TOF LiDAR Systems: A Review

Lidar De-snowing Method with Density and Intensity Fusion

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