Estimation of Driving State of Formula Car Based on Data Fusion and AUKF

Vehicle driving status information is essential to improve the safety and performance of formula racing, but obtaining high-precision driving parameters is often costly. Based on the adaptive unscented Kalman filter of filter fusion, this paper applied a low-cost and high-precision method to predict the lateral and longitudinal acceleration of a vehicle. This paper embeds part of the data of the Dugoff tire model into the filter structure, which greatly improves the data utilization rate and realizes the further optimization of the filter effect. By using the information fusion-based AUKF algorithm to estimate the driving state of a high-speed formula racing, accurate estimation results can be obtained. The results show that the filter effect of this algorithm significantly exceeds that of UKF.