Optimizing the Fuel Consumption of Autonomous Vehicles using Convex-Concave Programming

The recent introduction of electronic, sensing, positioning and information technologies into road transportation systems has allowed safer cruising and faster navigation of vehicles. The next frontier is the transformation of road transport into a highly automated system through the deployment of connected and automated vehicles. One of the important challenges to achieve this, is the trajectory optimization of an autonomous vehicle while traversing a specific road segment in order to minimize some metric of interest. In this context, we formulate the problem of optimal acceleration profile selection to minimize the fuel consumption of an autonomous vehicle as a discrete-time optimal control problem. To estimate fuel consumption, we consider a non-convex empirical metamodel that computes the output as a function of the acceleration and speed profile of the vehicle. As the resulting problem is non-convex, we construct a difference-of-convex functions representation of the fuel consumption metamodel and develop an iterative convex-concave programming procedure to solve the problem. Simulation results for various scenarios demonstrate that the proposed approach is considerably faster and provides better solutions compared to a state-of-practice nonlinear solver.