2022/06/30 by A. Bertipaglia, Bertipaglia, A., D. de Mol +7
Engineering · #Autonomous Vehicle Technology and Safety #FOS: Electrical engineering #Systems and Control (eess.SY) #Vehicle Dynamics and Control Systems #Vehicle emissions and performance #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2206.15119
openalex publication_date 2022/06/30 · openalex created_date 2022/07/03 · openalex updated_date 2026/07/28
This paper provides a comprehensive comparison of model-based and data-driven approaches and analyses the benefits of using measured tyre forces for vehicle sideslip angle estimation. The model-based approaches are based on an extended Kalman filter and an unscented Kalman filter, in which the measured tyre forces are utilised in the observation model. An adaptive covariance matrix is introduced to minimise the tyre model mismatch during evasive manoeuvres. For data-driven approaches, feed forward and recurrent neural networks are evaluated. Both approaches use the standard inertial measurement unit and the tyre force measurements as inputs. Using the large-scale experimental dataset of 216 manoeuvres, we demonstrate a significant improvement in accuracy using data-driven vs. model-based approaches. Tyre force measurements improve the performance of both model-based and data-driven approaches, especially in the non-linear regime of tyres.