Quantifying the impact of limited information and control robustness on connected automated platoons

Interaction between vehicle connectivity and automation has the potential to improve the safety, comfort, and energy efficiency of passenger road transportation. Differing approaches to connected automated vehicle following with and without limited preview information and worst-case rear-end collision robustness are presented. Given information on the preceding vehicle's current acceleration demand that may be coarse and discrete, a combined statistical and kinematic model is used to generate a prediction of future preceding vehicle motion. Model predictive control is then applied to produce a safe and smooth velocity trace for the ego vehicle, which is shown to improve energy efficiency. Simulation results compare acceleration, jerk, fuel efficiency, and road space utilization metrics of differing connected anticipative approaches with those of the intelligent driver model in 10-vehicle platoons.