The problem of eco-driving is analyzed for an urban traffic network in presence of signalized intersections. It is assumed that the traffic light timings are known and available to the vehicles via infrastructure-to-vehicle communication. This work provides a solution to the energy consumption minimization while traveling through a sequence of signalized intersections and always catching a green light. The optimal-control problem is non-convex because of the constraints coming from the traffic lights; therefore, a sub-optimal strategy to restore the convexity and solve the problem is proposed. Firstly, a pruning algorithm aims at reducing the optimization domain by considering only the portions of the traffic light's green phases that allow to drive in compliance with the city speed limits. Then, a graph is created in the feasible region in order to approximate the energy consumption associated with each available path in the driving horizon. Lastly, after the problem convexity is recovered, a simple optimization problem is solved on the selected path to calculate the optimal crossing times at each intersection. The optimal speeds are then suggested to the driver. The proposed sub-optimal strategy is compared with the optimal solution provided by dynamic programming for validation purposes. It is also shown that the low computational load of the presented approach enables robustness properties and results very appealing for online use.Extensive study and experimentation of several adaptive traffic control systems have been conducted over the past three decades. Strategies such as SCOOT [4] reduces traffic delay by about 20% in urban areas; SCATS and TUC [5] have been employed all over the world proving actual benefits in terms of congestion and emissions reduction. However, these strategies present some limitations in terms of traffic conditions responsiveness and sensor failure robustness.The state of the art in wireless communication, the deployment of dedicated communication protocols for vehicular ad-hoc networks, and the decreasing price of GPS receivers allow more and more to rely on communication between the different agents of an urban traffic network for the design of robust and efficient traffic control strategies [6]. Specifically, infrastructure-tovehicle (I2V) and vehicle-to-vehicle (V2V) communication attracted the attention of many because of their potential to enable fast and cheap advanced driving assistance systems (ADAS). Several international projects and groups (Drive C2X, iTetris, COMeSafety2, and PATH), involving both automotive manufacturers and research centers, have been working on the setup of the communication infrastructure and on the assessment of the effect of this technology on traffic management and energy consumption.Speed advisory for the urban environment was already proposed in the 1980s [7,8] as a very energy-efficient traffic management strategy and as a pioneer for the modern ADAS. The rather simple initial idea of placing a roadside sign upstream from an intersection, ind...

Safe-and eco-driving control for connected and automated electric vehicles using analytical state-constrained optimal solution.Abstract-Speed advisory systems have been proposed for connected vehicles in order to minimize energy consumption over a planned route. However, for their practical diffusion, these systems must adequately take into account the presence of preceding vehicles. In this paper, a safe-and eco-driving control system is proposed for connected and automated vehicles to accelerate or decelerate optimally while guaranteeing vehicle safety constraints. We define minimum inter-vehicle distance and maximum road speed limit as state constraints, and formulate an optimal control problem minimizing the energy consumption. Then, an analytical state-constrained solution is derived for realtime use. A feasible range of terminal conditions is established, and such conditions are adjusted to guarantee the existence of the analytical solution. The proposed system is evaluated through simulation for various driving scenarios of the preceding vehicle. Results show that it can significantly reduce energy consumption and also avoid collision without increasing trip time. Moreover, the proposed system can serve as an energy-efficient advanced cruise control by setting a short prediction horizon.

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As road transportation energy use and environmental impact are globally rising at an alarming pace, authorities seek in research and technological advancement innovative solutions to increase road traffic sustainability. The unclear and partial correlation between road congestion and environmental impact is promoting new research directions in traffic management. This paper aims to review the existing modeling approaches to accurately represent traffic behavior and the associated energy consumption and pollutant emissions. The review then covers the transportation problems and control strategies that address directly environmental performance criteria, especially in urban networks. A discussion on the advantages of the different methods and on the future outlook for the eco-traffic management completes the proposed survey.