Accurate real-time truck simulation via semirecursive formulation and Adams-Bashforth-Moulton algorithm

In this paper, a tailored four-step Adams-Bashforth-Moulton (ABM) algorithm is applied to a semirecursive formulation to perform a real-time simulation of a semitrailer truck. In the ABM algorithm, each integration step involves two function evaluations, namely predictor and corrector. This is fundamentally different when compared to the classic fourth-order Runge-Kutta (RK) integrator approach that contains four function evaluations. A semitrailer truck under investigation is modeled in term of a semirecursive method and simulated by using the presented ABM algorithm. The results show that the four-step ABM method can reduce CPU time almost 50% for solving the truck dynamics with very similar accuracy, in comparison to the fourth-order RK method. The presented ABM algorithm could be used in the semirecursive formulation to carry out accurate real-time simulation of medium-large vehicle systems.

Vehicle sideslip trajectory prediction based on time-series analysis and multi-physical model fusion

On highways,vehicles that swerve out of their lane due to sideslip can pose a serious threat to the safety of autonomous vehicles.To ensure their safety,predicting the sideslip trajectories of such vehicles is crucial.However,the scarcity of data on vehicle sideslip scenarios makes it challenging to apply data-driven methods for prediction.Hence,this study uses a physical model-based approach to predict vehicle sideslip trajectories.Nevertheless,the traditional physical model-based method relies on constant input assumption,making its long-term prediction accuracy poor.To address this challenge,this study presents the time-series analysis and interacting multiple model-based(IMM)sideslip trajectory prediction(TSIMMSTP)method,which encompasses time-series analysis and multi-physical model fusion,for the prediction of vehicle sideslip trajectories.Firstly,we use the proposed adaptive quadratic exponential smoothing method with damping(AQESD)in the time-series analysis module to predict the input state sequence required by kinematic models.Then,we employ an IMM approach to fuse the prediction results of various physical models.The implementation of these two methods allows us to significantly enhance the long-term predictive accuracy and reduce the uncertainty of sideslip trajectories.The proposed method is evaluated through numerical simulations in vehicle sideslip scenarios,and the results clearly demonstrate that it improves the long-term prediction accuracy and reduces the uncertainty compared to other model-based methods.