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.