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Fuel Optimized Predictive Following in Low Speed Conditions

The situation when driving in dense traffic and at low speeds is called Stop and Go. A controller for automatic following of the car in front could under these conditions reduce the driver's workload and keep a safety distance to the preceding vehicle through different choices of gear and engine torque. The aim of this thesis is to develop such a controller, with an additional focus on lowering the fuel consumption. With help of GPS, 3D-maps and sensors information about the slope of the road and the preceding vehicle can be obtained. Using this information the controller is able to predict future possible control actions and an optimization algorithm can then find the best inputs with respect to some criteria. The control method used is Model Predictive Control (MPC) and as the name indicate a model of the control object is required for the prediction. To find the optimal sequence of inputs, the optimization method Dynamic Programming choose the one which lead to the lowest fuel consumption and satisfactory following. Simulations have been made using a reference trajectory which was measured in a real traffic jam. The simulations show that it is possible to follow the preceding vehicle in a good way and at the same time reduce the fuel consumption with approximately 3 %.

Johan Jonsson


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