TY - JOUR
T1 - A Novel Fault-Tolerant Scheme for Multi-Model Ensemble Estimation of Tire Road Friction Coefficient With Missing Measurements
AU - Wang, Yan
AU - Zhang, Zhiguo
AU - Wei, Henglai
AU - Yin, Guodong
AU - Huang, Hailong
AU - Li, Boyuan
AU - Huang, Chao
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Accurate information on tire road friction coefficient (TRFC) is essential to autonomous driving systems. In this paper, a fault-tolerant estimation scheme is proposed to estimate TRFC in the case of missing measurements. First, a fault-tolerant unscented Kalman filter (FTUKF) is developed for estimating longitudinal and lateral tire forces in the condition of sensor signal loss. Then, longitudinal and lateral TRFCs are estimated separately with FTUKF based on tire forces information. Next, an event-driven multi-model fusion method based on the degree of data loss is designed to perform a weighted fusion of longitudinal and lateral TRFCs to further improve the estimation accuracy. Experiments with different working conditions are performed to demonstrate the validity of the fault-tolerant estimation framework. The results illustrate that the designed approach has higher estimation accuracy and strong adaptability under various roads.
AB - Accurate information on tire road friction coefficient (TRFC) is essential to autonomous driving systems. In this paper, a fault-tolerant estimation scheme is proposed to estimate TRFC in the case of missing measurements. First, a fault-tolerant unscented Kalman filter (FTUKF) is developed for estimating longitudinal and lateral tire forces in the condition of sensor signal loss. Then, longitudinal and lateral TRFCs are estimated separately with FTUKF based on tire forces information. Next, an event-driven multi-model fusion method based on the degree of data loss is designed to perform a weighted fusion of longitudinal and lateral TRFCs to further improve the estimation accuracy. Experiments with different working conditions are performed to demonstrate the validity of the fault-tolerant estimation framework. The results illustrate that the designed approach has higher estimation accuracy and strong adaptability under various roads.
KW - Autonomous driving systems
KW - event-driven fusion method
KW - fault-tolerant unscented Kalman filter
KW - tire road friction coefficient
UR - https://www.scopus.com/pages/publications/85178072509
U2 - 10.1109/TIV.2023.3336048
DO - 10.1109/TIV.2023.3336048
M3 - 文章
AN - SCOPUS:85178072509
SN - 2379-8858
VL - 9
SP - 1066
EP - 1078
JO - IEEE Transactions on Intelligent Vehicles
JF - IEEE Transactions on Intelligent Vehicles
IS - 1
ER -