## 5 Conclusions and Prospects  
Changes in driving style and the road environment are significant factors that influence eco-driving. This study newly integrates the elements of driving style and road gradient changes into the vehicle longitudinal control model, enabling the control system  
Figure 23: Typical slope section speed tracking under actual working conditions.  
Figure 22: Vehicle elevation changes under actual working conditions.  
to adaptively recognize various driving styles and road conditions for improved control outcomes. Compared to traditional cruise control systems, the accuracies of speed control and fuel consumption have been enhanced. The development of an eco-driving oriented vehicle longitudinal control model can reduce development costs and enhance development efficiency simultaneously. This approach serves as a practical method to achieve energy-saving driving and enhance road safety. The conclusions are as follows:  
1. The proposed spectral clustering driving style recognition method based on a Bi-LSTM autoencoder successfully distinguishes different types of driving styles under ecological driving behavior.
2. A fuzzy inference logic was devised to adaptively modify the control weights of the objective function taking into account factors such as driving style, slope, acceleration change rate, and vehicle speed.
3. The proposed DS-MPC control method is more accurate and economical than traditional control methods under real working conditions.
4. The proposed DS-MPC control method is highly versatile and yields significant and consistent optimization effects on fuel consumption, regardless of the slope and driving style, resulting in a fuel savings of 3% to 4% on average.  
This research is a study aimed to improve the speed control accuracy of heavy-duty truck driving assistance systems and simultaneously reduce fuel consumption. However, the DS-MPC control method proposed in this paper does not categorize and identify the driving styles of heavy vehicles under different road conditions (e.g., urban and rural areas). Therefore, there is a need to identify driving styles under different road conditions by exploring more accurate methods to initialize the raw labels of the data in future research. In addition, the control strategy proposed in this paper can be deeply optimized to improve control accuracy by combining asymptotic tracking and neural adaptive learning algorithms with anti-perturbation.  
Conceptualization, Y.Z. and H.C.; methodology, W.G.; software, Y.Z. and H.C.; validation, L.Y., X.C. and M.P.; formal analysis, X.C.; investigation, L.Y.; resources, X.C.; data curation, L.Y.; writing--original draft preparation, Y.Z.; writing--review and editing, L.Y. and W.G.; visualization, L.Y.; supervision, M.P.; project administration, W.G.; funding acquisition, M.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Guangxi University Sugarcane Research Fund] grant number [2022 GZB008] and the National Natural Science Foundation of China [U23A202599].  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.