# Adaptive Cruise Control for Eco-Driving Based on Model Predictive Control Algorithm
## 1 Introduction  
Reducing fuel consumption, carbon dioxide (\(CO_{2}\)), and other air pollution emission has been an impending sustainable problem facing the transportation sector, especially the automobile industry, which is currently stalemating over oil scarcity and environmental concerns. Therefore, policymakers and automobile manufacturers have been exploring numerous strategies and products to minimize fuel consumption and emissions. Approaches to achieving the reduction of fuel consumption are diverse, such as advanced engine technology, intelligent vehicle technology, and new energy technology, like pure electric vehicle and hybrid electric vehicle [1]. Among these strategies and products, eco-driving, as one of the new control technologies, has been widely noted because of its potential of reducing fuel consumption in whatever the local microscopic or global macroscopic level [2; 3; 4; 5; 6; 7; 8; 9]. The main concept of eco-driving is to offer the driver an optimal driving strategy or replace the driver with advanced driver assistance systems (ADAS,) to improve the fuel economy under the guise of satisfying the driving time and speed limits. The primary objective of eco-driving is to adapt the host vehicle to its driving surroundings, including road condition and traffic flow, so that the fuel consumption and exhaust emissions can be minimized. Eco-driving research related to the idea of controlling the vehicle speed for minimizing fuel consumption has been around a long time. Studies for optimizing vehicle cruise speed in terms of its driveline operation condition have been conducted, without considering the effect of trafficsurroundings and road information [10; 11]. Fuel economy for road vehicles not only depends on the performance of drivetrain inside the vehicle; it also depends on its operation--this was first proposed by [PERSON] and [PERSON] in 1977 [12]. Utilizing an optimal hybrid powertrain and traffic flow data obtained by intelligent transportation technologies, reducing the fuel consumption driving on the urban road can be achieved [13]. Fuel economy can be improved by forecasting required engine torque and optimized speed profile, with the combination of comprehensive traffic information and a model predictive controller [14]. However, the previously mentioned approaches are impractical to real-time optimization and calculation, because of lacking the integrated driving behavior before travel. In addition to optimal driving strategies in terms of automatic operation of a vehicle, a variety of eco-driving assistance functions have been applied in a real-time driving scenario. A green \"ECO\" indicator has been used for displaying if the current driving behavior is fuel economic or not. A research work to determine the optimum driving strategy of a vehicle based on eco-driving rules has been done by [PERSON] and [PERSON] [15]. However, only the model of the engine in terms of vehicle speed, gear ratio, and load is considered. Another work that fully responds to the road condition, upcoming traffic signals, and spacing between vehicles of the real driving scene can generate rough driving behavior advice, such as speed up or slow down, to the user [16]. Nevertheless, these eco-driving assistance approaches are more about giving driving guidance qualitatively, rather than providing quantitative velocity or acceleration data required for fuel economy assessment. The current existing eco-driving system can be classified into three categories: before the traveling scheme, during traveling scheme, and after traveling scheme according to the time of data processing. Due to a greater extent of real-time interaction and engagement with traffic flow and road conditions, the during-traveling scheme eco-driving system, as a branch of rapidly developed advanced driver assistance systems (ADASs), has been strikingly concerned academically. Adaptive cruise control (ACC) is an automated vehicle control system of ADASs, and safe car-following is the basic task of developing the ACC system.