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

Roman Meshcheryakov,Konrad Gromaszek,Mark Mamchenko,Maria Romanova,Yedilkhan Amirgaliyev,Andrey Iskhakov,Saygid Uvaysov

doi:10.3390/s22020609

Abstract

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.

Highlights

Almost all modern autonomous vehicles (AVs) are equipped with light detection and ranging (LiDAR) devices
Incorrect input from LiDAR can lead to the incorrect behavior of the AVs on the roads, traffic disruption, and accidents, posing risks to humans’ health and life
The “smart city” concept implies that a large number of unmanned vehicles will be driving simultaneously, and this poses the major problem of the usage of the LiDARs, the proven influence of the LiDARs on each other, i.e., the mutual interference of the signals from several LiDARs

Summary

Introduction

Almost all modern autonomous vehicles (AVs) are equipped with light detection and ranging (LiDAR) devices. This technology allows to obtain and process data on the remote objects using active optical (laser) systems. These sensors are physically based on the use of light absorption and scattering phenomena in optically transparent (and semi-transparent) media. These sensors are among those most exposed to external influences in self-driving systems [1,2]. LiDAR’s functioning can be influenced by the sunlight, low temperatures, and harsh weather conditions, e.g., rain, snow, fog, wind, etc., resulting in their performance degradation [3,4,5]

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