This paper presents an adaptive leading cruise control strategy for the automated vehicle (AV) and first considers its impact on the following human-driven vehicle (HDV) with diverse driving characteristics in the unified optimization framework for improved holistic energy efficiency. The car-following behaviors of HDV are statistically calibrated using the Next Generation Simulation dataset. In a typical single-lane car-following scenario where AVs and HDVs share the road, the longitudinal speed control of AVs can substantially reduce the energy consumption of the following HDV by avoiding unnecessary acceleration and braking. Moreover, apart from the objectives including car-following safety and traffic efficiency, the energy efficiencies of both AV and HDV are incorporated into the reward function of reinforcement learning (RL). The specific driving pattern of the following HDV is learned in real-time from historical speed information to predict its acceleration and power consumption in the optimization horizon. A comprehensive simulation is conducted to statistically verify the positive impacts of AV on the holistic energy efficiency of the mixed traffic flow with uncertain and diverse human driving behaviors. In freeway driving scenarios, simulation results indicate that the holistic energy efficiency is improved by an average of 6.03% and 6.41% compared to the reference control algorithms, specifically, RL without HDV consideration and model predictive control. These improvements highlight the significance of our approach in optimizing energy efficiency for mixed traffic on freeways.

Electric vehicles are considered the most effective solution to the petroleum crisis and reduction of air pollution. In order to enhance energy efficiency and battery lifetime, this paper designs a predictive cruise controller (EC) for electric vehicles. Road information and traffic preview are employed for velocity planning while minimizing energy usage, maintaining battery health, and avoiding collision with a lead vehicle. To enable real-time implementation, we apply a model predictive control (MPC) framework formulated in space domain, and approximation and relaxation are introduced to obtain a smooth nonlinear program. Simulation results indicate that the proposed controller yields suboptimal performance as compared to the globally optimal solution. For higher practicability on real-life scenarios, we develop an enhanced EC that is capable of optimizing the stopping of the ego vehicle. According to the car-following studies where the lead vehicle is driven using real-life data, the enhanced EC achieves ${7.14}{\%}$ energy saving and ${29.81}{\%}$ battery life extension when compared to the intelligent driving model. The computation time of ${40}{ms}$ per MPC update also demonstrates its potential for real-time applications.

The push towards sustainable transportation emphasizes vehicular energy efficiency in mixed traffic scenarios. A research hotspot is the cooperative control of connected and automated vehicles (CAVs), particularly in contexts involving the uncertainties of human-driven vehicles (HDVs). Cooperative control strategies are pivotal in improving driving safety, traffic efficiency, and reducing energy consumption. Our study introduces a cooperative control strategy for CAVs in mixed traffic based on the multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm. We use the intelligent driver model (IDM) to calibrate and model human driving behaviors with 1737 car-following events from the Next Generation Simulation (NGSIM) dataset for their high frequency in real-world driving. The reward function of MATD3 integrates safety, traffic efficiency, passenger comfort, and energy efficiency. An action mask scheme is incorporated to prevent collisions, thereby boosting learning efficiency. Monte carlo simulation results show that our strategy outperforms IDM and model predictive control in improving energy efficiency by an average of 7.73% and 3.38% respectively. Furthermore, our framework offers extended benefits to HDVs, which achieve improved energy efficiency following the CAVs’ control pattern. A case study further demonstrate that a ‘moderate’ driving style results in lower energy consumption, effectively linking human behaviors to energy efficiency.