###### Abstract
The findings indicate that fuel consumption is cut by at least 3.5% without any impact on the vehicle's performance. Given that the initial design of fuzzy rules typically necessitates continuous experimental verification, modification, and optimization, which is both time-consuming and inefficient, machine learning methods have been widely adopted and advanced in the research of driving style recognition. By combining feature selection, machine learning models can effectively select the most representative features of the driving style from a vast array of behavioral data, resulting in a more precise identification of the driver's style. To determine the features that significantly affect fuel consumption, [PERSON] et al. [29] suggested a linear regression model that takes into account ten features that directly reflect fuel consumption, aiming to identify variations in driving behavior. [PERSON] et al. [30] investigated the features of driving cycles by employing genetic algorithms, wherein they selected six out of twelve features under sampling windows of 146 s and 80 s. [PERSON] et al. [31] employed a Gaussian mixture model to determine the feature distribution that influences driving style and employed Bayesian information to evaluate and analyze the correlation of the selected features. It is necessary to take into account the road slope for eco-friendly driving while on the road. When compared to the traditional adaptive cruise control (ACC) system, the ecological driving technology that incorporates road slope information in advance has been found to enhance fuel economy by 4.5%. Consequently, the integration of road slope factors into the longitudinal control methods of automobiles has garnered significant research interest. [PERSON] et al. [32] introduced a hybrid model predictive control (HMPC) theory and employed a mixed logic dynamic (MLD) framework, a specialized hybrid system modeling technique, for the development of the upper controller. The proposed control method's effectiveness was verified through the use of speed tracking at various slopes and NEDC loops for speed tracking. [PERSON] et al. [33] proposed an energy-optimal adaptive cruise control method that takes into account factors such as speed limits, road slope, and travel time during the optimization process, resulting in the planning of an optimal speed trajectory. [PERSON] et al. [34] proposed a distributed model predictive control method for vehicles driving on highways with various slopes. When compared to benchmark testing, the proposed strategy can result in saving more than 21% of fuel for the entire vehicle. This article aims to improve the speed control accuracy of heavy-duty truck driving assistance systems and simultaneously reduce fuel consumption by constructing a high-precision vehicle longitudinal control model guided by ecological driving. The structure of this article is as follows. In Section 2, the process of establishing an MPC speed control model that integrates driving style recognition methods and road slope is discussed in detail. In Section 3, the data sources are examined and the efficacy of the model is confirmed. Subsequently, the model built in this article was simulated and validated by setting different road slope conditions. Simultaneously, in Section 4, an accurate analysis is conducted on the effectiveness of the DS-MPC control method under real driving conditions. Finally, the main findings are summarized and future work is discussed in Section 5.