On generating driving trajectories in urban traffic to achieve higher fuel efficiency

Abstract

In this thesis, a toolkit with the purpose of generating optimal policies for driving a vehicle with information about upcoming traffic signals has been developed. The toolkit can be used to investigate how to generate the optimal velocity profile with upcoming traffic signals based on a model of second-by- second fuel consumption. To this purpose, we employ an instantaneous fuel consumption model and formulate an optimization problem for fuel mini- mization. Following the problem formulation, we explore different numerical ways to solve the minimization problem by discretization. The Runge-Kutta 4 th Order Method (RK4) is chosen to numerically deal with the differential con- straints in the optimization problem since RK4 gives higher resolution with fewer partitions when discretizing along the time horizon. Then, we turn to Direct Transcription with RK4 Steps and Parallel Shooting (DTRPS) with which we translate the minimization problem to a nonlinear programming (NLP) problem. We also include an extensive case study for a door-to-door trip with differ- ent traveling settings: travel during which there are no traffic lights; travel with one light and travel with two lights. The result shows the capability of the toolkit. For a specific setting of a trip, an optimal profile of instantaneous velocity, acceleration and fuel consumption is generated to achieve the lowest fuel consumption for the entire trip.