In view of the problem of relative displacement caused by wheel slippage during the operation of autonomous wheeled clam harvesting equipment on intertidal mudflats， which affects the path tracking accuracy of the autonomous driving system， a slip prediction method based on vehicle kinematics and dynamics models was proposed. The vehicle's motion trajectory was derived according to the four-wheel chassis kinematic model， and the walking dynamics model was established by incorporating tire lateral and longitudinal force characteristics. The calculation methods for longitudinal slip and lateral slip were clarified. An autonomous driving test platform was designed， consisting of perception， planning， and control layers. The platform collected position， steering angle， and wheel speed data using an RTK-GNSS system and relevant sensors. Longitudinal and lateral slip experiments were conducted to investigate the slip characteristics of the harvesting equipment in the intertidal environment. The longitudinal slip experiment analyzed the effects of motor PWM （pulse width modulation） and load on slip ratio， and a longitudinal slip model was established through data fitting. The lateral slip experiment explored the slip characteristics under different steering angles， leading to the fitting of a lateral slip model. To simplify calculations， the slip coefficient function was expressed in the form of a Taylor polynomial. The model's accuracy was validated through experiments， with the error between the actual and predicted trajectories being less than 16%， significantly reducing slip-related interference errors in autonomous driving on mudflats. Through experimental analysis and model optimization， this study improves the accuracy of slip prediction and provides a reference for achieving high-precision navigation for clam harvesting equipment in soft ground environments. Future research could focus on real-time parameter estimation of the slip model and the dynamics modeling of ground-contact components to enhance the equipment.