  Advanced driver assistance systems are critically dependent on reliable and
accurate information regarding a vehicles' driving state. For estimation of
unknown quantities, model-based and learning-based methods exist, but both
suffer from individual limitations. On the one hand, model-based estimation
performance is often limited by the models' accuracy. On the other hand,
learning-based estimators usually do not perform well in "unknown" conditions
(bad generalization), which is particularly critical for semitrailers as their
payload changes significantly in operation. To the best of the authors'
knowledge, this work is the first to analyze the capability of state-of-the-art
estimators for semitrailers to generalize across "unknown" loading states.
Moreover, a novel hybrid Extended Kalman Filter (H-EKF) that takes advantage of
accurate Artificial Neural Network (ANN) estimates while preserving reliable
generalization capability is presented. It estimates the articulation angle
between truck and semitrailer, lateral tire forces and the truck steering angle
utilizing sensor data of a standard semitrailer only. An experimental
comparison based on a full-scale truck-semitrailer combination indicates the
superiority of the H-EKF compared to a state-of-the-art extended Kalman filter
and an ANN estimator.
