TY - GEN

T1 - Estimation of Maximum Friction Coefficient Using Recurrent Artificial Neural Networks

AU - Lampe, Nicolas

AU - Ziaukas, Zygimantas

AU - Westerkamp, Clemens

AU - Jacob, Hans-Georg

N1 - Funding Information: The authors would like to thank the Dr. Jürgen and Irmgard Ulderup foundation for funding this project.

PY - 2022/10/31

Y1 - 2022/10/31

N2 - Knowledge of vehicle dynamics and in particular the maximum friction coefficient is required for the optimization of advanced driver assistance systems and the implementation of autonomous driving. Since the maximum friction coefficient cannot be measured directly, estimating this coefficient based on available sensors is a field of interest. In particular, model-based approaches based on Kalman filter derivates are used. However, their accuracy is limited by the accuracy of the physical model. Due to the real-time capability required, more detailed modeling is not possible. In addition, system identification and a robust filter design are needed. As a result, data-based approaches are gaining popularity in vehicle dynamics, which are also suitable for estimating the maximum friction coefficient. In this paper, maximum friction coefficient estimation is presented using recurrent artificial neural networks (RANN) based on vehicle sensors. In order to avoid incorrect estimations during low vehicle excitation, an excitation monitoring approach is introduced. Typical longitudinal and lateral driving maneuvers on different road surfaces simulated in IPG CarMaker with a Dacia Duster are used for training, validation and testing of the RANN. Finally, the data-based approach shows improved results compared to the model-based approach of a sensitivity-based unscented Kalman filter (sUKF) from previous works.

AB - Knowledge of vehicle dynamics and in particular the maximum friction coefficient is required for the optimization of advanced driver assistance systems and the implementation of autonomous driving. Since the maximum friction coefficient cannot be measured directly, estimating this coefficient based on available sensors is a field of interest. In particular, model-based approaches based on Kalman filter derivates are used. However, their accuracy is limited by the accuracy of the physical model. Due to the real-time capability required, more detailed modeling is not possible. In addition, system identification and a robust filter design are needed. As a result, data-based approaches are gaining popularity in vehicle dynamics, which are also suitable for estimating the maximum friction coefficient. In this paper, maximum friction coefficient estimation is presented using recurrent artificial neural networks (RANN) based on vehicle sensors. In order to avoid incorrect estimations during low vehicle excitation, an excitation monitoring approach is introduced. Typical longitudinal and lateral driving maneuvers on different road surfaces simulated in IPG CarMaker with a Dacia Duster are used for training, validation and testing of the RANN. Finally, the data-based approach shows improved results compared to the model-based approach of a sensitivity-based unscented Kalman filter (sUKF) from previous works.

KW - automotive

KW - maximum friction coefficient

KW - neural networks

KW - parameter estimation

KW - vehicle dynamics

UR - http://www.scopus.com/inward/record.url?scp=85142181620&partnerID=8YFLogxK

U2 - 10.1145/3560453.3560459

DO - 10.1145/3560453.3560459

M3 - Conference contribution

AN - SCOPUS:85142181620

SP - 28

EP - 35

BT - 2022 The 3rd International Conference on Robotics Systems and Vehicle Technology (RSVT)

PB - Association for Computing Machinery (ACM)

CY - New York, NY, United States

T2 - 3rd International Conference on Robotics Systems and Vehicle Technology, RSVT 2022

Y2 - 22 July 2022 through 24 July 2022

ER -