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.

Probabilistic models for forecasting and assessing the reliability of navigation parameters in intelligent transportation systems are proposed. The relevance of the study is driven by the need to enhance the reliability of robotic transportation systems operating in dynamically changing urban environments. In such environments, sensor failures, signal distortions, and a high degree of data uncertainty are possible. The proposed approach is based on the application of probabilistic analysis methods and statistical control to detect anomalies in navigation parameters such as coordinates, speed, and orientation. The concept of navigation data reliability is introduced as a quantitative measure characterizing the degree of correspondence between the measured parameters and the actual state of the system. Key validity criteria are defined: confidence probability, significance level and confidence coefficients. To improve the reliability of parameter assessment, a combination of statistical analysis methods and filtering algorithms is proposed. Forecasting involves preliminary data processing aimed at smoothing noise and verifying data consistency. Outlier detection is performed using statistical methods, including confidence intervals and variance minimization. An forecasting model based on the Kalman filter and dynamic updating of probabilistic estimates has been developed. The integration of various methods into a unified system minimizes the impact of random and systematic errors, ensuring more accurate assessment of navigation parameters. The proposed approach is applicable to the development of navigation systems for autonomous robots and unmanned vehicles, enabling them to adapt to external conditions without the need for precise a priori data.