# An Eco-Driving Controller Based on Intelligent Connected Vehicles for Sustainable Transportation
## 1 Introduction
In terms of eco-driving, [PERSON] et al. [17] proposed an eco-driving system for multiple signal intersection scenarios, with the objective of reducing fuel consumption by optimizing the vehicular trajectory. [PERSON] et al. [18] proposed an enhanced velocity planning algorithm for connected vehicles in the proximity of signalized intersections, which ensures the effectiveness of energy and efficiency. [PERSON] and [PERSON] [19] proposed a vehicular velocity control strategy that can improve the fuel efficiency for connected autonomous vehicles in the scenario of driving through an intersection. [PERSON] et al. [20] developed a hybrid algorithm that integrates deep Q-learning with policy gradient for the vehicles driving along signalized corridors. This algorithm can reduce fuel consumption by learning continuous longitudinal acceleration and deceleration. However, their attentions were on the velocity optimizations of the vehicle in the signalized intersection. [PERSON] et al. [21] developed an eco-driving strategy based on an MPC model for heavy trucks, which can realize fuel conservation while ensuring safe driving spacing. However, this study did not consider the lateral control of the vehicle. [PERSON] et al. [22] proposed an optimal method for the speed profiles of vehicles on curved roads to realize the purpose of eco-driving. [PERSON] et al. [23] proposed a system-optimal approach to improve the efficiency of fuel usage and traffic flow for the benefits of the entire road network. [PERSON] et al. [24] proposed a vehicular velocity optimization algorithm based on real-time data from global positioning system (GPS) and radar on a vehicle. Experiments in a driving simulator showed that the algorithm was effective in reducing fuel consumption. These studies consider the strategies of a vehicle that is not affected by other vehicles for eco-driving. In summary, it is worth noting that the current cooperative car-following systems of ICVs still lack the effective technology with the integration of considerable strategies in exhaust emissions for sustainable transportation. In our work, an eco-driving controller for ICVs was studied to reduce fuel consumption and exhaust emission by considering real-time data and lateral and longitudinal control, so that the environmental transportation can be effectively achieved. The main contributions of this work are summarized as follows:  
* The real-time data from multiple ICVs were applied to propose the eco-driving controller, which includes longitudinal and lateral speeds, desired acceleration, and forward steering angle. With these data, the proposed controller can respond quickly and accurately to the behavior of preceding vehicles and thereby reduce unnecessary braking and moderating acceleration. * Based on more roadside data in V2X communication, the proposed eco-driving controller uses model predictive control theory that considers road curvature to optimize the car-following behavior of ICVs. * With the above two advantages, a novel eco-driving objective function considering the optimal energy consumption, longitudinal and lateral velocity was established for ICVs in complex roads. Furthermore, fuel consumption and carbon dioxide emissions can be reduced significantly. The remainder of this paper is organized as follows. In Section 2, an eco-driving controller based on ICVs is proposed and constructed. Then, the co-simulation platform of ICVs is designed and established in Section 3, where the ROS and PreScan software are applied in the development of the platform. In Section 4, a three-car-following platoon is verified by the co-simulation platform, and the experimental results are demonstrated and analyzed. Finally, Section 5 concludes the paper.