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This paper presents a safety-enhanced eco-driving strategy for connected and autonomous vehicles (CAVs), which is implemented by a hierarchical and distributed framework. The driving risk field, shockwave theory, and motion planning and control method are integrated into this framework to optimize the trajectories of CAVs on a signalized arterial under mixed traffic flow, with the aim of reducing the driving risk and fuel consumption of CAVs simultaneously, while ensuring traffic efficiency. The optimization procedure is mainly composed of two parts: long-term trajectory planning based on optimal control and short-term trajectory control based on model predictive control, which makes the strategy more adaptable to the various traffic conditions. The results show that the proposed framework can effectively reduce the safety risk that vehicles are exposed to and their fuel consumption by 18%–24% and 20%–27%, respectively. Furthermore, it reveals that conventional eco-driving strategies may result in negative safety issues when only considering the impact of preceding vehicles on the eco-CAV. However, these negative impacts can be eliminated when the impacts of following vehicles on the eco-CAV are taken into account. In addition, the sensitivity analysis on the Market Penetration Rate (MPR) of CAVs and traffic demand is performed. The results show that the framework is robust and can work under various traffic conditions (including under-saturated and over-saturated ones) and different MPRs. • Propose a safety-enhanced eco-driving strategy for CAVs under mixed traffic flow. • Develop two algorithms to alleviate intersection queuing effects for eco-driving. • While ensuring traffic efficiency, optimize CAVs' fuel consumption and driving risk. • Perform numerical experiments to assess the benefits of the proposed framework. • Reveal conventional eco-driving strategy may result in safety risks. [ABSTRACT FROM AUTHOR]

Highlights • An eco-driving model for mixed traffic flow including automated vehicles and human driven vehicles. • Platoon based cooperation between automated vehicles and human driven vehicles. • Dynamic platoon splitting and merging method in terms of cooperation. • Cooperation behaviour significantly smooth the traffic flow and reduce the travel time. Abstract The advancements in communication and sensing technologies can be exploited to assist the drivers in making better decisions. In this paper, we consider the design of a real-time cooperative eco-driving strategy for a group of vehicles with mixed automated vehicles (AVs) and human-driven vehicles (HVs). The lead vehicles in the platoon can receive the signal phase and timing information via vehicle-to-infrastructure (V2I) communication and the traffic states of both the preceding vehicle and current platoon via vehicle-to-vehicle (V2V) communication. We propose a receding horizon model predictive control (MPC) method to minimise the fuel consumption for platoons and drive the platoons to pass the intersection on a green phase. The method is then extended to dynamic platoon splitting and merging rules for cooperation among AVs and HVs in response to the high variation in urban traffic flow. Extensive simulation tests are also conducted to demonstrate the performance of the model in various conditions in the mixed traffic flow and different penetration rates of AVs. Our model shows that the cooperation between AVs and HVs can further smooth out the trajectory of the latter and reduce the fuel consumption of the entire traffic system, especially for the low penetration of AVs. It is noteworthy that the proposed model does not compromise the traffic efficiency and the driving comfort while achieving the eco-driving strategy. [ABSTRACT FROM AUTHOR]

Highlights • We highlight the energy saving potential of connected and automated vehicles. • Anticipative/Collaborative driving of CAVs can contribute to 3–20% energy saving. • With higher penetration, there will be system wide influences by such “eco-CAV”s. • CAVs offer opportunities for cooperative driving further improving energy efficiency. Abstract Connected and automated vehicles (CAV) are marketed for their increased safety, driving comfort, and time saving potential. With much easier access to information, increased processing power, and precision control, they also offer unprecedented opportunities for energy efficient driving. This paper is an attempt to highlight the energy saving potential of connected and automated vehicles based on first principles of motion, optimal control theory, and a review of the vast but scattered eco-driving literature. We explain that connectivity to other vehicles and infrastructure allows better anticipation of upcoming events, such as hills, curves, slow traffic, state of traffic signals, and movement of neighboring vehicles. Automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and save energy. Opportunities for cooperative driving could further increase energy efficiency of a group of vehicles by allowing them to move in a coordinated manner. Energy efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles. [ABSTRACT FROM AUTHOR]

Highlights • The algorithm provides a constantly updated set of speed profiles. • It optimizes energy consumption, running time and risk of delay in arrival. • Manual driving uncertainty is taken into account using fuzzy modelling. • Energy savings can be achieved while the risk of delay is controlled. • The higher the risk of delay the higher the energy consumption. Abstract Eco-driving is an energy efficient traffic operation measure that may lead to important energy savings in high speed railway lines. When a delay arises in real time, it is necessary to recalculate an optimal driving that must be energy efficient and computationally efficient. In addition, it is important that the algorithm includes the existing uncertainty associated with the manual execution of the driving parameters and with the possible future traffic disturbances that could lead to new delays. This paper proposes a new algorithm to be executed in real time, which models the uncertainty in manual driving by means of fuzzy numbers. It is a multi-objective optimization algorithm that includes the classical objectives in literature, running time and energy consumption, and as well a newly defined objective, the risk of delay in arrival. The risk of delay in arrival measure is based on the evolution of the time margin of the train up to destination. The proposed approach is a dynamic algorithm designed to improve the computational time. The optimal Pareto front is continuously tracked during the train travel, and a new set of driving commands is selected and presented to the driver when a delay is detected. The algorithm evaluates the 3 objectives of each solution using a detailed simulator of high speed trains to ensure that solutions are realistic, accurate and applicable by the driver. The use of this algorithm provides energy savings and, in addition, it permits railway operators to balance energy consumption and risk of delays in arrival. This way, the energy performance of the system is improved without degrading the quality of the service. [ABSTRACT FROM AUTHOR]

In this paper, a multi-level, comprehensive control technique for a heavy duty vehicle, capable of reducing fuel consumption while complying with engine constraints on both operating conditions and smoke emissions has been developed. The higher control layer, using road data, engine and vehicle parameters, computes the optimal speed and gear shifting profiles through an off-line, dual step dynamic programming algorithm. Since fuel consumption alone could be potentially decreased by driving at lower speeds and thus increasing travel time, this last aspect is also taken into account in the presented algorithm. The lower control layer, operating in real time, computes suitable powertrain control signals assuring the tracking of both reference speed and gear trajectories, while respecting the constraints imposed on engine operation. After the general description of the optimal control problem aimed at minimizing fuel consumption, an approach for its solution, implemented in the higher control layer, is presented. Afterwards, the lower control layer is described. In conclusion, the effectiveness of the developed control structure and the achievable improvement in terms of fuel consumption in comparison with a traditional fixed-point cruise controller are assessed through simulations in different driving scenarios. [ABSTRACT FROM AUTHOR]

Whilst driving is inherently a safety–critical task, awareness of fuel-efficient driving techniques has gained popularity in both the public and commercial domains. Green driving, whether motivated by financial or environmental savings, has the potential to reduce the production of greenhouse gases by a significant amount. This paper focusses on the interaction between the driver and their vehicle – what type of eco-driving information is easy to use and learn whilst not compromising safety. A simulator study evaluated both visual and haptic eco-driving feedback systems in the context of hill driving. The ability of drivers to accurately follow the advice, as well as their propensity to prioritise it over safe driving was investigated. We found that any type of eco-driving advice improved performance and whilst continuous real-time visual feedback proved to be the most effective, this modality obviously reduces attention to the forward view and increases subjective workload. On the other hand, the haptic force system had little effect on reported workload, but was less effective that the visual system. A compromise may be a hybrid system that adapts to drivers’ performance on an on-going basis. [ABSTRACT FROM AUTHOR]

Eco-Driving has great potential in reducing the fuel consumption of road vehicles, especially under the connected and automated vehicles (CAVs) environment. Traditional model-based Eco-Driving methods usually require sophisticated models and cannot deal with complex driving scenarios. This paper proposes a hybrid reinforcement learning (RL) based Eco-Driving algorithm considering both the longitudinal acceleration/deceleration and the lateral lane-changing operations. A deep deterministic policy gradient (DDPG) algorithm is designed to learn the continuous longitudinal acceleration/deceleration to reduce the fuel consumption as well as to maintain acceptable travel time. Collecting the critic's value of each single lane from DDPG and integrating the information of adjacent lanes, a deep Q-learning algorithm is developed to make the discrete lane-changing decision. Together, a hybrid deep Q-learning and policy gradient (HDQPG) method is developed for vehicles driving along multi-lane urban signalized corridors. The method can enable the controlled vehicle to learn well-established longitudinal fuel-saving strategies, and to perform appropriate lane-changing operations at proper times to avoid congested lanes. Numerical experiments show that HDQPG can reduce fuel consumption by up to 46% with marginal or no increase of travel times. • Continuous state space for longitudinal accelerations and discrete state space for lateral lane changing. • A hybrid deep Q-learning and policy gradient (HDQPG) method is developed for Eco-Driving. • HDQPG can enable vehicles to learn well-established Eco-Driving strategies. • HDQPG can save fuel consumption by 46% compared with the baseline model. • HDQPG outperforms existing RL-based Eco-Driving methods. [ABSTRACT FROM AUTHOR]