###### Abstract  
Developing high-precision vehicle longitudinal control technology guided by ecological driving represents a highly promising yet challenging endeavor. It necessitates the fulfillment of the driver's operational intentions, precise speed control, and reduced fuel consumption. In light of this challenge, this study presents a novel vehicle longitudinal control model that integrates real-time driving style analysis and road slope prediction. First, it utilizes spectral clustering based on Bi-LSTM automatic encoders to identify driver driving styles. Next, it examines the driving environment and predicts the current slope of the vehicle. Additionally, a fuzzy controller is designed to optimize control performance, adapt to various driving styles and slopes, and achieve better fuel efficiency. The research results indicate that the DS-MPC control model developed in this paper can effectively distinguish various driving modes and has high speed control accuracy while saving 3.27% of fuel. ecological driving; slope behavior; vehicle longitudinal control; heavy-duty; model predictive control; fuel-saving +
Footnote †: journal: Medical  
can more precisely regulate vehicle speed, thereby enhancing the safety of driving. [PERSON] [11] devised a longitudinal controller consisting of two parts. The external controller determines the target speed, whereas the internal controller is responsible for determining the throttle and brake. [PERSON] et al. [12] introduced a highly effective system linearization scheme that integrates a novel real-time point updating method with traditional linearization techniques. This integration leads to a reduction in steady-state errors and overshoot, ultimately improving the control performance of MPC controllers. [PERSON] et al. proposed a new integral robust format for the asymptotic tracking of mismatched uncertain nonlinear systems [13] and a neural adaptive learning algorithm for constrained nonlinear systems with interference suppression [14], achieving the stability and accuracy of the control systems under multi-source disturbances. [PERSON] et al. [15] suggested an integrated braking and steering MPC controller that can execute accurate path tracking while retaining high computational efficiency, thereby enhancing the precision of speed control and vehicle safety. Meanwhile, ecological driving technology [16; 17], which is often overlooked, has the potential to significantly improve vehicle fuel consumption. According to research, ecological driving can decrease fuel consumption by 15% to 25% and reduce greenhouse gas emissions by at least 30% [18]. In contrast, engines and vehicles utilizing the latest technology are estimated to save approximately 10% to 12% in total fuel costs [19]. Ecological driving encompasses various factors, such as driving speed, acceleration, deceleration, route selection, and idle speed [20; 21; 22]. The variations in these factors have resulted in diverse driving styles. [PERSON] et al. introduced a driving style classification system [23] which utilizes lateral and longitudinal acceleration behavior. [PERSON] et al. [24] developed an objective function for risk measurement during driving, which integrates driving style and relevant safety factors into ADAS through optimal control. The concept of safe operation was integrated into the optimal control problem in the form of a penalty function. To obtain the optimal reference operation strategy, certain parameters in the driving style were utilized to optimize the control parameters. With its robustness and easy-to-understand characteristics, fuzzy logic (FL) improves the efficiency of driving style recognition and is frequently utilized in such applications [25; 26; 27]. An FL algorithm was proposed by [PERSON] et al. [28] to assess the ideal pedal operation of hybrid vehicles. This algorithm continuously monitors the accelerator and brake pedal operations, performs necessary corrections, and provides tactile feedback to the driver.