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Fuel-minimizing vehicle trajectory specification in the presence of traffic lightswith certain or stochastic switching times

Kalogianni Ioanna

Πλήρης Εγγραφή

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Περίληψη

Driving style of road vehicles has a significant impact on the fuel economy, hence the recent term eco-driving to denote a driving style that reduces fuel consumption. In the last decades, autonomous driving, but also vehicular communications are becoming more and more important. One application of vehicle connectivity is to receive information about the next signal switching time when vehicles approach a traffic light. Based on this information, appropriately developed systems (or apps), known as GLOSA (Green Light Optimal Speed Adaptation), compute a fuel-efficient velocity profile for the vehicle to cross the traffic lights, e.g. without stopping. The main purpose of this thesis is to generate optimal (fuel-minimising) trajectories for vehicles crossing a signalized junction, with traffic signals operated in either fixed-time or real-time mode. In the case of fixed-time signals, the next switching time is known beforehand; in real-time signals, the next switching time is decided in real time based on the prevailing traffic conditions and is therefore uncertain in advance. This thesis approaches the problem by using traffic lights' information and calculating a trajectory and a velocity profile for the vehicle, based on the vehicle’s initial state (position and speed) and a fixed final destination state (downstream of the junction). In the case of fixed signals, an appropriate optimal control problem is formulated and solved analytically via the Pontryagin's Minimum Principle (PMP). In the case of real-time signals, availability of a time-window of possible signal switching times, along with the corresponding probability distribution, is assumed, and the problem is cast in the format of a stochastic optimal control problem and is solved numerically using Stochastic Dynamic Programming techniques.

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