# An Eco-Driving Controller Based on Intelligent Connected Vehicles for Sustainable Transportation
## 1 Introduction  
In recent years, environmental problems caused by vehicle exhaust emissions have attracted widespread attention from industry to academia. The ICVs that integrate sensing, wireless communication, and control technologies are used to solve the above problems. Human-driven vehicles (HVs) are controlled by human beings with determining velocity while driving, which requires human drivers to observe with eyes and make decisions with brains. The process is time consuming because of people's thinking and actions taken. Therefore, the HVs must keep a large spacing with the preceding vehicle to guarantee the driving safety [1, 2]. It is well known that less driving resistance causes lower fuel consumption and less environmental pollution [3]. Keeping a small spacing between vehicles can make the air resistance minimized while driving. This is especially apparent in the air resistance of vehicles at a high velocity [4]. Besides, ICVs can be controlled automatically, according to commands derived from information of the surrounding traffic state, which can reduce the system response time significantly. That is to say, ICVs can keep a small spacing from other vehicles, theoretically, can improve road capacity, and can reduce fuel consumption and environmental pollution. In addition, ICVs can relieve the pressureof human drivers effectively, improve the convenience of driving, and enhance traffic safety. Therefore, ICVs can solve the urban traffic congestion problems and environmental pollution issues [5]. Extensive studies have been carried out to establish the effective car-following models of ICVs by controlling vehicular velocities [6; 7; 8]. [PERSON] [9] proposed a car-following model with a velocity control function, which considered the delay caused by environmental and human factors to ensure driving safety. This model defined a control strategy based on the spacing between two vehicles and a set of differential-difference equations. [PERSON] et al. [10] proposed a confined full velocity difference (c-FVD) model to solve the problem of acceleration overshoot of the FVD model in a specific traffic scenario, which limited the acceleration of the existing FVD model to produce milder vehicular trajectories. These classical models explain the car-following behavior of two adjacent vehicles. However, they did not fully consider the influence of other preceding vehicles and the environment on the car-following behavior; thereby, the advantages of ICVs cannot be sufficiently utilized. Some scholars have applied wireless communication technology to car-following models. [PERSON] et al. [11] implemented a cooperative adaptive cruise control (CACC) system based on the feed-forward controller, which enables a small spacing between vehicles while ensuring safety. [PERSON] et al. [12] developed and analyzed a controller for CACC when the fleet has a leading vehicle. Besides, [PERSON] et al. [13] proposed a CACC system with combining precise location and V2X communication, which has a positive impact on reducing congestion in dense traffic conditions. These studies aimed to improve traffic conditions by applying the information of preceding vehicles to adjust the behaviors of following vehicles. Recently, various machine learning approaches have been used to study car-following models. [PERSON] et al. [14] proposed a data-driven car-following model considering information from a field data set. [PERSON] et al. [15] proposed a hybrid model to learn the car-following behavior from the real driving data of human drivers. These machine learning-based models have higher requirements for the dataset, which lead to limitations in adaptability for more application scenarios. Most of the above studies have not considered the combination of autonomous driving technology and V2X communication technology in their solutions, which drive us to bridge the important gap in this paper. Eco-driving is often used to refer to a vehicle operation that minimizes energy consumption and carbon dioxide emissions [16].