Slip　parameter　prediction　is　crucial　for　motion　planning　and　control　of　unmanned　skid-steering　vehicles　in　off-road　environments.　Slip　parameter　prediction　methods　based　on　nonlinear　observers　and　those　based　on　machine　learning　models　both　have　limitations　under　various　conditions.　Therefore,　this　paper　presents　a　multi-layer　Adaptive　Unscented　Kalman　Filter　(AUKF)　slip　parameter　prediction　method　based　on　a　fusion　framework　of　nonlinear　observers　and　machine　learning　models.　The　method　first　constructs　the　0-layer　AUKF　using　the　vehicle　kinematic　model　and　sensor　data　to　initialize　the　slip　parameters.　Then,　with　the　input　of　the　desired　sequences　of　wheel　speeds　generated　by　the　autonomous　driving　system,　the　1-layer　AUKF　is　constructed　by　combining　the　machine　learning　predictive　model　and　running　N　times　to　obtain　the　future　slip　parameter　sequence.　Experimental　data　was　collected　by　driving　on　paved　and　dirt　roads　with　a　skid-steering　vehicle.　The　experimental　results　show　that　the　method　in　this　paper　outperforms　methods　based　on　nonlinear　observers　in　terms　of　slip　parameter　prediction　accuracy　when　the　prediction　time　domain　is　long.　Furthermore,　when　faced　with　unknown　conditions,　this　method　shows　superior　robustness　compared　to　methods　based　on　machine　learning　models.　　©　2024　IEEE.