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.

Abstract-Given the importance of continuous traffic flow forecasting in most of Intelligent Transportation Systems (ITS) applications, where every new traffic data become available in every few minutes or seconds, the main objective of this study is to perform a multi-step ahead traffic flow forecasting that can meet a trade-off between accuracy, low computational load, and limited memory capacity. To this aim, based on adaptive Kalman filtering theory, two forecasting approaches are proposed. We suggest solving a multi-step ahead prediction problem as a filtering one by considering pseudo-observations coming from the averaged historical flow or the output of other predictors in the literature. For taking into account the stochastic modeling of the process and the current measurements we resort to an adaptive scheme. The proposed forecasting methods are evaluated by using measurements of the Grenoble south ring.

An important problem in traffic estimation, forecasting, and control is the reconstruction of densities in portions of the road links not equipped with sensors. In this paper, and based on ideas from Morarescu and Canudas-de Wit [2011], we use a deterministic constrained model that reduces the number of possible affine dynamics of the system and preserves the number of vehicles in the network. In particular we reformulate the idea in Morarescu and Canudas-de Wit [2011] with the correct number of feasible modes, and introduce the concept of graph constrained-CTM observer, which is used to reconstruct the densities from the Grenoble south ring use case that contains 45 cells organized in 9 links, and is simulated using a calibrated AISUM micro-simulator. This work is performed in connection with HYCON2 traffic show case (www.hycon2.eu), and with the Grenoble Traffic Lab (GTL)

International audienceTransportation is responsible for a substantial fraction of worldwide energy consumption and greenhouse gas emissions and is the largest sector after energy production. However, while emissions from other sectors are generally decreasing, those from transportation have increased since 1990. Reducing the impact of transportation is a task that is inherently associated with the improvement of energy efficiency, particularly for passenger cars that contribute to almost half of the whole sector

Abstract-In this paper we present a robust mode selector for the uncertain graph-constrained Switching Mode Model (SMM), which we use to describe the highway trafficd e n s i t y evolution. Assuming an uncertain speed of the congestion wave, the proposed selector relies on a transition digraph suitably incorporating the present and historical statistical traffici nformation, to determine the most probable current mode of the SMM. Its effectiveness is demonstrated on the problem of traffic density reconstruction via a switching observer, in an instrumented 2.2 km highway section of Grenoble south ring in France.