UC Berkeley

Connected and Automated Vehicles (CAVs) improve driving automation by exploiting evolving connectivity technologies which enable communication among vehicles (V2V), communication between vehicles and the road infrastructure such as traffic lights (V2I), and communication between vehicles and the cloud (V2C). The goal of this dissertation is to present novel control algorithms for CAVs which harness the untapped CAV potential of remote computations, forecasts, historical data, automation and coordination with other vehicles and infrastructure. First, we introduce a control and planning system architecture suitable for CAVs with focus on improving the energy efficiency of the vehicle. Then, for each level of the architect, we present planning and control algorithms for the longitudinal motion and powertrain control exploiting the connectivity, and provide a set of thorough simulations and experiments aimed at quantifying energy efficiency improvements for automated cars and railways. The experimental results are complemented with a theoretical and simulation analysis of the stability and feasibility of the closed loop system under nominal and perturbed conditions. By using the proposed control methodology, our experimental results show that safe and energy efficient driving in both arterial and highway settings can be achieved. In particular, we achieve up to 20% energy savings on both settings.