With the continuous increase in the number of vehicles,traffic accidents are occurring more frequently, which poses a great threat to peoples` lives and to property safety. In order to improve improve driving safety, an intelligent assisted driving method based on mathematical modeling and model predictive control is proposed. This method applies clustering algorithms and Bayesian networks for identifying the driving styles and evaluating the road adhesion level. In addition, the results of the simulations which were carried out could be applied to the construction of mathematical models for vehicle driving, combined with model predictive control methods for achieving the control of assisted driving systems. The obtained results show that the vehicle tracking accuracy of the proposed assisted driving method remains within the range of 0.4m, and the response time is only 0.64s. To that, the average value for the passenger comfort assessment exceeded 90 points out of a maximum score of 100 points. This indicates that the proposed assisted driving method can effectively enhance the user’s assisted driving experience, by achieving a higher accuracy and enabling a safer intelligent driving.
Mathematical modelling, MPC, Clustering, Intelligent driving, Assistance.
Yan XU, Feng ZHANG, Yan-nan SUN, "Intelligent Assisted Driving Method Based on Mathematical Modeling and MPC Algorithm", Studies in Informatics and Control, ISSN 1220-1766, vol. 34(2), pp. 15-25, 2025. https://doi.org/10.24846/v34i2y202502
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