Data-driven cooperative control of connected and automated vehicles (CAVs) has gained extensive research interest as it can utilize collected data to generate control actions without relying on parametric system models that are generally challenging to obtain. Existing methods mainly focused on improving traffic safety and stability, while less emphasis has been placed on energy efficiency in the presence of uncertainties and diversities of human-driven vehicles (HDVs). In this brief, we employ a Data-EnablEd Predictive Control (DeePC) scheme to address the eco-driving of mixed traffic flows with diverse behaviors of human drivers. Specifically, by incorporating first-order physics, we propose the physics-augmented DeePC (PA-DeePC) approach to handle substantial system variations and uncertainties. Then, a novel optimization framework, avoiding conventional reference trajectories during the receding optimization process, is proposed to further reduce the holistic energy consumption of mixed traffic flows. Simulation results demonstrate the effectiveness of our approach in accurately capturing random human driver behaviors and reducing holistic energy consumption, while ensuring driving safety and traffic efficiency. Furthermore, the proposed optimization framework achieves substantial reductions in energy consumption, i.e., average reductions of 4.14%, 4.97%, and 9.29% when compared to the benchmark algorithms.

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.

Eco-cruising control of vehicles is a potential approach for improving vehicle energy efficiency and reducing travel time. However, many eco-cruising studies merely focused on vehicle longitudinal speed optimization but overlooked the lane change maneuvers, which may impair the benefits of eco-cruising when the vehicle encounters the slowly moving preceding vehicle. This study proposes a flexible eco-cruising strategy (FECS) with efficient driving lane planning and speed optimization capabilities simultaneously for connected and automated vehicles. The FECS is designed with a hierarchical control framework, where the first layer uses the Dijkstra algorithm to plan an efficient driving lane sequence considering the long-term effect of the preceding vehicles, then guides the second layer to optimize the vehicle’s speed for saving energy using trigonometric speed profile. The optimized driving trajectory is implemented in the third layer by regulating the speed and yaw angle for guaranteeing safe inter-vehicle distance when uncertainties are present. Finally, stochastic simulation with randomized traffic flows and typical case analysis based on real-world traffic data are conducted to demonstrate the performance of the FECS. The results manifest FECS’s capability of lowering driving costs in moderate-flow and free-flow traffic. However, we note that the benefits are less pronounced in congested-flow traffic.

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.

This paper presents a safe learning-based eco-driving framework tailored for mixed traffic flows, which aims to optimize energy efficiency while guaranteeing system constraints during real-system operations. Even though reinforcement learning (RL) is capable of optimizing energy efficiency in intricate environments, it is challenged by safety requirements during both the training and deployment stages. The lack of safety guarantees impedes the application of RL to real-world problems. Compared with RL, model predicted control (MPC) can handle constrained dynamics systems, ensuring safe driving. However, the major challenges lie in complicated eco-driving tasks and the presence of disturbances, which pose difficulties for MPC design and constraint satisfaction. To address these limitations, the proposed framework incorporates the tube-based enhanced MPC (RMPC) to ensure the safe execution of the RL policy under disturbances, thereby improving the control robustness. RL not only optimizes the energy efficiency of the connected and automated vehicle in mixed traffic but also handles more uncertain scenarios, in which the energy consumption of the human-driven vehicle and its diverse and stochastic driving behaviors are considered in the optimization framework. Simulation results demonstrate that the proposed algorithm achieves an average improvement of 10.88% in holistic energy efficiency compared to the RMPC technique, while effectively preventing inter-vehicle collisions when compared to the RL algorithm.