Eco-driving Strategies Research Articles

Intersection eco-driving strategies under mixed traffic environment: An novel cooperation of traffic signal and vehicle trajectory planning

Intersection eco-driving strategies under mixed traffic environment: An novel cooperation of traffic signal and vehicle trajectory planning

An Eco-Driving Strategy at Multiple Fixed-Time Signalized Intersections Considering Traffic Flow Effects

To encourage energy saving and emission reduction and improve traffic efficiency in the multiple signalized intersections area, an eco-driving strategy for connected and automated vehicles (CAVs) considering the effects of traffic flow is proposed for the mixed traffic environment. Firstly, the formation and dissipation process of signalized intersection queues are analyzed based on traffic wave theory, and a traffic flow situation estimation model is constructed, which can estimate intersection queue length and rear obstructed fleet length. Secondly, a feasible speed set calculation method for multiple signalized intersections is proposed to enable vehicles to pass through intersections without stopping and obstructing the following vehicles, adopting a trigonometric profile to generate smooth speed trajectory to ensure good riding comfort, and the speed trajectory is optimized with comprehensive consideration of fuel consumption, emissions, and traffic efficiency costs. Finally, the effectiveness of the strategy is verified. The results show that traffic performance and fuel consumption benefits increase as the penetration rate of CAVs increases. When all vehicles on the road are CAVs, the proposed strategy can increase the average speed by 9.5%, reduce the number of stops by 78.2%, reduce the stopped delay by 82.0%, and reduce the fuel consumption, NOx, and HC emissions by 20.4%, 39.4%, and 46.6%, respectively.

Assessing the impact of driving behaviors and traffic conflicts on vehicle emissions at non-signalized intersections using a trajectory-based computational framework

Vehicle emissions can rise due to traffic conflicts and aggressive driving behaviors, such as frequent acceleration and deceleration. This issue is particularly pronounced at non-signalized intersections with a high proportion of non-motorized vehicles. In this study, we propose a framework that integrates a microscopic vehicle emission model with trajectory data. By utilizing trajectory data collected from a non-signalized intersection in Shanghai, we analyzed vehicle emissions linked to driving behaviors and traffic conflicts. Our findings reveal that pre-braking at the entrance of non-signalized intersections can significantly reduce vehicle emissions, lowering them by nearly 80 % for straight maneuvers. However, this reduction is less substantial for turning maneuvers. Additionally, conflicts involving more than two types of targets lead to a significant increase in vehicle emissions. On average, stop-and-go emissions are 1.13 % higher than those resulting from traffic conflicts. Interestingly, when non-motorized vehicles constitute more than 80 % of the traffic volume, stop-and-go emissions fall below those generated by traffic conflicts. The results of this study provide valuable insights for optimizing eco-driving strategies and advancing towards a low-carbon transportation system.

Multiple Electric Components Health-Aware Eco-Driving Strategy for Fuel Cell Hybrid Electric Vehicle Based on Soft Actor-Critic Algorithm

Multiple Electric Components Health-Aware Eco-Driving Strategy for Fuel Cell Hybrid Electric Vehicle Based on Soft Actor-Critic Algorithm

Electric vehicle eco-driving strategy at signalized intersections based on optimal energy consumption

Electric vehicles (EVs), which are a great substitute for gasoline-powered vehicles, have the potential to achieve the goal of reducing energy consumption and emissions. However, the energy consumption of an EV is highly dependent on road contexts and driving behavior, especially at urban intersections. This paper proposes a novel ecological (eco) driving strategy (EDS) for EVs based on optimal energy consumption at an urban signalized intersection under moderate and dense traffic conditions. Firstly, we develop an energy consumption model for EVs considering several crucial factors such as road grade, curvature, rolling resistance, friction in bearing, aerodynamics resistance, motor ohmic loss, and regenerative braking. For better energy recovery at varying traffic speeds, we employ a sigmoid function to calculate the regenerative braking efficiency rather than a simple constant or linear function considered by many other studies. Secondly, we formulate an eco-driving optimal control problem subject to state constraints that minimize the energy consumption of EVs by finding a closed-form solution for acceleration/deceleration of vehicles over a time and distance horizon using Pontryagin’s minimum principle (PMP). Finally, we evaluate the efficacy of the proposed EDS using microscopic traffic simulations considering real traffic flow behavior at an urban signalized intersection and compare its performance to the (human-based) traditional driving strategy (TDS). The results demonstrate significant performance improvement in energy efficiency and waiting time for various traffic demands while ensuring driving safety and riding comfort. Our proposed strategy has a low computing cost and can be used as an advanced driver-assistance system (ADAS) in real-time.

Multi-performance enhanced eco-driving strategy for connected fuel cell hybrid electric bus based on stein soft actor-3-critic

Eco-driving strategies increasingly emphasize enhancing multi-performance including safety, comfort, durability, and traffic efficiency under optimal energy-saving. This paper introduces a multi-performance enhanced eco-driving strategy for connected fuel cell hybrid electric buses (FCHEBs) based on the stein soft actor-3-critic (SSA3C) algorithm. By leveraging the correlation between the international roughness index (IRI) and weighted root mean square acceleration (WRMSA), energy management system (EMS) and adaptive cruise control (ACC) are integrated the to enhancing multi-performance including vertical comfort and energy efficiency. To address the insufficient action representation and overestimation of Q-values of traditional soft actor-critic (SAC) algorithm, improvements are made to the actor and critic networks using stein variational gradient descent (SVGD) and clipped triple Q-learning (3C), respectively. Composite cycles integrating the standard cycle and IRI are developed to enhance the strategy's generalization. Experimental results indicate that the proposed eco-driving strategy, while maintaining dynamics, safety, durability, and efficiency, efficiently achieves 98.1 % energy savings and equivalent comfort level as dynamic programming (DP). Ablation experiments on vertical comfort demonstrate improvements of 1.24 % in energy efficiency and 2.94 % in WRMSA. Finally, the effectiveness of the network improvements and the adaptability of the proposed strategy are demonstrated through comparative experiments.

Developing an eco-driving strategy in a hybrid traffic network using reinforcement learning.

Eco-driving has garnered considerable research attention owing to its potential socio-economic impact, including enhanced public health and mitigated climate change effects through the reduction of greenhouse gas emissions. With an expectation of more autonomous vehicles (AVs) on the road, an eco-driving strategy in hybrid traffic networks encompassing AV and human-driven vehicles (HDVs) with the coordination of traffic lights is a challenging task. The challenge is partially due to the insufficient infrastructure for collecting, transmitting, and sharing real-time traffic data among vehicles, facilities, and traffic control centers, and the following decision-making of agents involved in traffic control. Additionally, the intricate nature of the existing traffic network, with its diverse array of vehicles and facilities, contributes to the challenge by hindering the development of a mathematical model for accurately characterizing the traffic network. In this study, we utilized the Simulation of Urban Mobility (SUMO) simulator to tackle the first challenge through computational analysis. To address the second challenge, we employed a model-free reinforcement learning (RL) algorithm, proximal policy optimization, to decide the actions of AV and traffic light signals in a traffic network. A novel eco-driving strategy was proposed by introducing different percentages of AV into the traffic flow and collaborating with traffic light signals using RL to control the overall speed of the vehicles, resulting in improved fuel consumption efficiency. Average rewards with different penetration rates of AV (5%, 10%, and 20% of total vehicles) were compared to the situation without any AV in the traffic flow (0% penetration rate). The 10% penetration rate of AV showed a minimum time of convergence to achieve average reward, leading to a significant reduction in fuel consumption and total delay of all vehicles.

Assessing the impact of passenger compliance behavior in CAVs on environmental benefits

The potential of connected autonomous vehicles (CAVs) to offer significant environmental benefits is increasingly acknowledged. Nevertheless, it is debatable concerning CAV implementation as an eco-driving strategy, given that large-scale deployment has not yet been realized and studies relevant to emission reduction have yielded inconsistent results. Our study investigates the effects of implementing CAVs on emission reduction and focuses on passenger reactions regarding vehicular platooning. We employ microsimulation to examine the environmental effects associated with different levels of CAV penetration, complemented by a VR experiment that evaluates rider adherence to CAV systems. The findings indicate that higher CAV penetration rates and greater passenger compliance contribute to reductions in vehicle emissions, yet there is a concurrent rise in PM10 levels when the fleet size exceeds seven vehicles. This study reveals the trade-off between CAV deployment for emission reduction and on-road environmental protection, emphasizing the vital role of passenger compliance in environmental advantages.

Intersection eco-driving for automated vehicles: SMPC-based strategies for handling leading vehicle starting-up uncertainties

Driving at intersections often differs from regular car-following scenarios, particularly in terms of start-up speed uncertainties. Extracting useful information from these uncertainties is crucial for improving traffic and energy efficiency, ultimately achieving eco-driving goals for automated vehicles. However, traditional car-following models are inadequate for describing intersection driving behaviors, while current eco-driving strategies often lack a thorough consideration of intersection traffic uncertainties. To this end, a new eco-driving strategy based on stochastic model predictive control (SMPC) is proposed, specifically addressing uncertainties in the starting-up behavior of leading vehicle (LV). To begin, a naturalistic driving dataset is constructed for a queuing scenario at a signalized intersection, enabling the establishment of probabilistic models for LV starting-up behavior. Next, an SMPC-based motion planning algorithm is introduced to facilitate optimal control of the ego vehicle (EV), directly incorporating the LV’s uncertainties. Specifically, a phantom LV model is introduced to depict LV’s starting-up probability in its initial preparing stage, and then in the accelerating stage a gaussian process regression (GPR) approach is employed for more accurate prediction of LV acceleration. Then the algorithm’s effectiveness is validated through stochastic and playback simulations in SUMO and Matlab. The playback simulations using conditions from naturalistic driving demonstrate that the algorithm significantly enhances energy efficiency, achieving savings of 21% and 37% compared to electric and fueled vehicles in real-world scenarios, respectively. Surprisingly, despite expectations, the algorithm can also improve traffic efficiency by an average of 17.9% and 13.6% for electric and fueled vehicles, respectively. Finally, the road experiment validates that our algorithm can achieve total savings of 15.8% energy in our experimental electric vehicle compared to real drivers. This algorithm has the potential to serve as an effective eco-driving solution for automated vehicle longitudinal planning and control in urban settings.

An improved eco-driving strategy for mixed platoons of autonomous and human-driven vehicles

The emergence of autonomous vehicles (AVs) could transform traffic flow characteristics and affect energy consumption subsequently. The primary objective of this study is to propose an improved eco-driving strategy for the mixed platoons by exploring the impacts of AVs on energy consumption characteristics. Initially, leveraging precise AV control, this study utilizes a Model Predictive Control (MPC) system to optimize AV trajectories, aiming to minimize energy consumption within mixed platoons of AVs and human-driven vehicles (HDVs). Given the differing collaborative capabilities of HDVs and AVs in mixed platoons, exceptional situations may arise at intersections where not all platoon vehicles cross the stop line during the same green light cycle (GLC). To address these challenges, we then propose a splitting approach to optimize the platoon operation mode. This approach allows for flexible splitting decisions based on AVs market penetration and current traffic conditions. The simulation results indicate that the energy consumption reduction ranges from 15.35% to 35.39% and 2.75–3.64% compared to the traditional mode and the eco-driving mode, respectively. In addition, through an analysis of the energy consumption of various vehicle orders within a mixed platoon, we determined that positioning the second AV in the middle platoon maximizes stability, resulting in a more consistent energy consumption pattern. These findings highlight the advantages of the strategy in terms of energy consumption, providing theoretical support management of mixed platoons.

Deep Reinforcement Learning Based Integrated Eco-Driving Strategy for Connected and Automated Electric Vehicles in Complex Urban Scenarios

Deep Reinforcement Learning Based Integrated Eco-Driving Strategy for Connected and Automated Electric Vehicles in Complex Urban Scenarios

Eco-Driving Strategy Optimization for High-Speed Railways Considering Dynamic Traction System Efficiency

Eco-Driving Strategy Optimization for High-Speed Railways Considering Dynamic Traction System Efficiency

An eco-driving strategy for autonomous electric vehicles crossing continuous speed-limit signalized intersections

The rapid advancement of Vehicle-to-Everything communication (V2X) technology presents opportunities for enhancing traffic energy efficiency. With V2X, this paper introduces an eco-driving strategy for autonomous electric vehicles to navigate continuous signalized intersections. This strategy incorporates the realistic powertrain's working efficiency and constraints into the vehicle motion optimization. In the domain of the global path, we formulate a co-optimization problem of vehicle motion and powertrain operation to tap the vehicle energy-saving potential and derive an optimal velocity profile, considering constraints related to queue release and limit speed in intersections. In the short rolling time domain, we propose a predictive control approach for vehicle speed tracking and real-time powertrain control. Simulation results under various scenarios show the advantages of the proposed method in terms of energy consumption compared to the baseline and existing methods.

Multi-objective coordinated control strategy for mixed traffic with partially connected and automated vehicles in urban corridors

In the urban corridor with a mixed traffic composition of connected and automated vehicles (CAVs) alongside human-driven vehicles (HDVs), vehicle operations are intricately influenced by both individual driving behaviors and the presence of signalized intersections. Therefore, the development of a coordinated control strategy that effectively accommodates these dual factors becomes imperative to enhance the overall quality of traffic flow. This study proposes a bi-level structure crafted to decouple the joint effects of the vehicular driving behaviors and corridor signal offsets setting. The objective of this structure is to optimize both the average travel time (ATT) and fuel consumption (AFC). At the lower-level, three types of car-following models while considering driving modes are presented to illustrate the desired driving behaviors of HDVs and CAVs. Moreover, a trigonometry function method combined with a rolling horizon scheme is proposed to generate the eco-trajectory of CAVs in the mixed traffic flow. At the upper-level, a multi-objective optimization model for corridor signal offsets is formulated to minimize ATT and AFC based on the lower-level simulation outputs. Additionally, a revised Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to identify the set of Pareto-optimal solutions for corridor signal offsets under different CAV penetration rates (CAV PRs). Numerical experiments are conducted within a corridor that encompasses three signalized intersections. The performance of our proposed eco-driving strategy is validated in comparison to the intelligent driver model (IDM) and green light optimal speed advisory (GLOSA) algorithm in single-vehicle simulation. Results show that our proposed strategy yields reduced travel time and fuel consumption to both IDM and GLOSA. Subsequently, the effectiveness of our proposed coordinated control strategy is validated across various CAV PRs. Results indicated that the optimal AFC can be reduced by 4.1%–32.2% with CAV PRs varying from 0.2 to 1, and the optimal ATT can be saved by 2.3% maximum. Furthermore, sensitivity analysis is conducted to evaluate the impact of CAV PRs and V/C ratios on the optimal ATT and AFC.

Energy-optimal Speed Regulation for CAVs in Urban Mixed Traffic with Induced Lane Changes

Existing urban energy-optimal vehicle speed regulation (eco-driving) studies were mostly carried out on single-lane roads or did not consider lane changes, delivering overidealized eco-driving results. In addition to lane changes that every vehicle may encounter on roads, the eco-driving connected automated vehicles (CAVs) are subject to induced types of lane changes of manually-driven vehicles (MVs), a cut-in of an MV to the front of a CAV from an adjacent lane, and an escape of an MV from behind a CAV to an adjacent lane. So far, very few studies have considered the impact of lane changes while designing the ecodriving strategies and evaluated the eco-driving performance with respect to lane-changing disturbances in credible simulation environments; and almost no existing work has addressed both lateral and longitudinal disturbances for eco-driving. This paper has developed a generic and deployable eco-driving strategy for CAVs under a unified rolling-horizon optimal control framework that can deal with both longitudinal and lateral disturbances of MVs without assuming communications between CAVs and MVs. The performance of this disturbance-rejection eco-driving strategy was thoroughly evaluated with regard to a multilane urban network, using the microscopic traffic simulator SUMO. The strategy was demonstrated very effective and robust, and similar results were little reported before.

Eco-Driving Strategy Design of Connected Vehicle among Multiple Signalized Intersections Using Constraint-enforced Reinforcement Learning

Eco-Driving Strategy Design of Connected Vehicle among Multiple Signalized Intersections Using Constraint-enforced Reinforcement Learning

Eco-driving strategy for connected automated vehicles in mixed traffic flow

Mixed traffic flow is a prevalent phenomenon in the trend of connected automated vehicles (CAVs), where a diverse set of road users, including cars, motorcycles, bicycles, pedestrians, and even animals, share the road infrastructure. This coexistence poses a range of challenges, not limited to traffic safety, efficiency, and environmental sustainability. Compared with the traditional traffic streams, the controllability of connected and automated vehicles within mixed traffic offers new possibilities for eco-driving. As CAV technologies continue to flourish, this study explores the imperative of constructing eco-roads within a mixed traffic framework and optimizing eco- driving strategies to enhance vehicle energy efficiency and reduce emissions. We extended the concept of mixed traffic flow to incorporate scenarios involving animal crossings, introducing an eco-road-based green mixed traffic model. Analyzing the driving behaviors of both autonomous and manually-driven vehicles within a vehicular network ecosystem, we proposed an eco- road driving model that includes vehicle-following and lane-changing behaviors. From the perspective of dynamic programming, we conducted a discrete analysis to create an energy- saving driving model apt for mixed traffic conditions, with Q-Learning serving as the optimal solver. We further validated our theoretical framework through simulations conducted on eco- roads in Shanghai, taking into account the inherent risks brought about by the crossing of wildlife. Our empirical results indicated that the recommended energy-saving strategies could potentially reduce fuel consumption by 6–11%. Interestingly, the energy-saving effects are amplified with an increasing density of networked autonomous vehicles (CAVs) within the mixed traffic environment. Our findings strengthen the feasibility of our model and the efficacy of our algorithmic approach, confirming that the described driving strategies hold great promise for significantly improving energy efficiency in the domain of connected vehicles under the premise of ensuring the safety of driving and wildlife.

Learning eco-driving strategies from human driving trajectories

Eco-driving represents a promising avenue for mitigating energy consumption in road transportation. To enhance the applicability of learning-based eco-driving strategies, this study presents a novel framework that employs offline reinforcement learning in eco-driving control. This framework enables a vehicle agent to acquire eco-driving behavior by leveraging real-world human driving trajectories. Specifically, the human driving trajectories, along with the corresponding traffic signal timing scheme, obtained from empirical data, are utilized to construct a comprehensive Markov Decision Process (MDP) dataset for offline policy training. To accommodate learning from sub-optimal human-driving data, a Conservative Q-learning (CQL) algorithm is deployed. Subsequently, the proposed offline learning method is compared with alternative learning-based, model-based, and rule-based approaches, effectively illustrating the feasibility of offline learning and the efficacy of the CQL algorithm. Notably, the energy consumption is demonstrated to be improved by 67.3% compared to a behavioral car-following model, with only marginal compromise to travel efficiency. Furthermore, a sensitivity analysis is conducted, revealing the generalizability of the offline learning-based method across various simulation configurations and even diverse energy consumption models.

Efficient self-driving control for lead vehicle following in a mixed traffic environment

Autonomous control for vehicles is rapidly developed since it not only improves the mixed traffic flow but also decreases the traffic accidents and the energy consumption. Especially self-driving technology has been attention since it progressively plays an important role in prompting comport driving condition as well as safety. In addition, eco-driving strategy is considered as the energy efficient driving technology of electric vehicles (EVs) since the eco-driving can increase the fuel economy of the limited power capacity of battery. This research introduces an energy efficient EVs control strategy with Model predictive control (MPC) and deep neural networks (DNN). MPC and DNN are combined to solve the optimal problem that is to minimize battery consumption and maximize the energy efficiency during self-driving. The estimated vehicle state information by DNN and the ratio of power consumption at the previous state are fed into the next state information of the MPC controller. Thus, the controller can optimize the energy consumption ratio with respect to the state values such as the vehicle state and the battery state of charge. The hybrid control algorithm was evaluated using PGDrive which is a virtual vehicle driving environment, and simulation results showed that the MPC controller with DNN can efficiently reduce the battery consumption and increase the energy efficient up to 1.7%.

A Comprehensive Eco-Driving Strategy for CAVs with Microscopic Traffic Simulation Testing Evaluation.

In this paper, a comprehensive deterministic Eco-Driving strategy for Connected and Autonomous Vehicles (CAVs) is presented. In this setup, multiple driving modes calculate speed profiles that are ideal for their own set of constraints simultaneously to save fuel as much as possible, while a High-Level (HL) controller ensures smooth and safe transitions between the driving modes for Eco-Driving. This Eco-Driving deterministic controller for an ego CAV was equipped with Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) algorithms. This comprehensive Eco-Driving strategy and its individual components were tested by using simulations to quantify the fuel economy performance. Simulation results are used to show that the HL controller ensures significant fuel economy improvement as compared to baseline driving modes with no collisions between the ego CAV and traffic vehicles, while the driving mode of the ego CAV was set correctly under changing constraints. For the microscopic traffic simulations, a 6.41% fuel economy improvement was observed for the CAV that was controlled by this comprehensive Eco-Driving strategy.