Regulation Compliance and Suspect Identification in Formula 1 Racing- A Case Study

Abstract This paper presents a novel approach for suspect identification in Formula 1 racing. The energy required to drive the vehicle and the energy available from the power sources while the vehicle is in the Drag Reduction Systems (DRS) zone are considered as key parameters in this approach. It uses the data from qualification runs of about 14 cars over 20 races in the 2022 race calendar. The proposed approach is sensitive enough to detect the changes in vehicle setting that is sufficient enough to influence the energy demand while on a straight part of the track. This novel approach can be used to check the compliance between Pre and Post-race Parc-ferme checks and identify suspects violating technical regulations during dynamic events.