Discrete dynamic optimization in automated driving systems to improve energy efficiency in cooperative networks

Predictive and energy efficient driving styles considerably reduce fuel consumption and emissions of vehicles. Vehicle-to-vehicle and vehicle-to-infrastructure (V2X) communication provide information useful to further optimize fuel economy especially in urban conditions. This work summarizes an optimization approach integrating V2X information in the optimization of longitudinal dynamics. Besides the dimensions distance and velocity also the dimension time is reflected in discrete dynamic programming, which is based on a three-dimensional state space. Upcoming signal states of traffic signals are reflected in the optimization to implement an efficient pass through at intersections. Furthermore, simulated average driving behavior defines a reference for optimized velocity trajectories. This excludes optimization results strongly deviating from average behavior. The approach is implemented in a vehicle in a real-time capable way. In a field test the vehicle approaches a V2X traffic light and the optimization reduces fuel consumption by up to 15 % without increasing travel time.