Are the regulations relating to electromagnetic compatibility (EMC) sufficient to ensure the safety of all autonomy systems? EMC is one of the critical factors influencing the proper functioning of a vehicle and its safety. However, the safety of autonomous vehicles from the perspective of EMC has not been comprehensively researched to date. The purpose of this article is to evaluate whether the currently imposed requirements are adequate. To assess research methods and requirements, it is necessary to determine the electromagnetic environment of vehicles. For this purpose, identification tests of the vehicle’s operating environment were conducted. The article presents the results of research on both the internal and external electromagnetic environments of vehicles. These studies were carried out using normative methods. Despite the significant importance of EMC and the existence of defined normative requirements, the results of the conducted research indicate that some devices available on the market, including those unrelated to vehicle autonomy, fail to meet these standards. Consequently, devices responsible for vehicle safety must be resistant to such electromagnetic exposures. In extreme cases, the malfunction of a single vehicle component due to inadequate electromagnetic compatibility could lead to the disruption of vehicle functions or driver disorientation. Such occurrences pose a direct threat to road traffic safety and the well-being of passengers.

Although reliable schedule adherence is pivotal for service quality and passenger planning in urban transit, realized arrivals often deviate from timetables in non Gaussian and time-dependent ways. This study provides a probabilistic–statistical assessment of how actual arrivals accord with schedules, thereby addressing the analytical gap of stop level comparisons across time of day and weekday strata. Using one month of real-time GNSS records matched to static GTFS for Warsaw bus line 112, we analyzed 8.6 million validated stop events. Delay distributions were characterized by robust statistics (median, interquartile range, skewness, kurtosis) and normality diagnostics (Lilliefors, Anderson–Darling, Jarque– –Bera), revealing heavy tails up to ±20 min and invalidating Gaussian assumptions. Non parametric tests (Kruskal–Wallis; Dunn–Šidák) showed statistically significant but practically small differences in median delay across seven time-of-day bands and weekdays (largest shift ≈ 1.2 min; typical deviations within ±1 min). We then clustered stop-level delay profiles (medians by time band) with K-means to uncover four interpretable punctuality archetypes (punctual, evening catch up, pre-schedule variability, and afternoon peak bottleneck), mapped along both travel directions and examined by weekday. We used these profiles to link delay patterns to infrastructure and operations (e.g., initial segments, intersections, recurring congestion) and furnish actionable outputs: targeted buffer allocation, identification of segments for control, and interpretable features to enhance learning-based arrival time prediction. Although demonstrated on a single line, the methodology is general and transferable to other networks with comparable GNSS and GTFS data.