Multi-Season Formula 1 Lap Dataset with Race-Control-Based Safety Car Labels (2022-2025)

Containing lap-level race data for the 2022-2025 Formula 1 seasons, the year-race-session data were collected using FastF1, public interface that provides access to timing and race information publicly available similarly on the web. Include only official race sessions! The data for each event were downloaded and then all of the events were merged into a single structured dataset. After eliminating invalid or incomplete laps and cleaning out missing values, the final dataset contains 89,980 driver lap observations. Each line represents one driver on one laps. The dataset includes such contextual and performance variables as lap number, race progression, tire compound, tire life, driver position, team ID, environmental variables including (air temperature, track temperature, humidity, rainfall) etc. We applied certain basic feature engineering processes in order to make the data model-friendly. Safety Car (SC) and Virtual Safety Car (VSC) labels were constructed by parsing official race control messages and aligning them with lap-level records. This makes it possible to model rare events at the level of the lap. The dataset also supports forward-looking targets (for instance, detection of a Safety Car within the next few laps) for probabilistic risk forecasting. The dataset's structure allows experiments with event-level validation. This means that entire Grand Prix events can be separated during training and testing to avoid leakage between laps in the same event. The data set is intended for academic research, it is a set of processing and structured derivative of public interface data FastF1 that was obtained via timing.