Safely Optimizing Highway Traffic with Robust Model Predictive Control-based Cooperative Adaptive Cruise Control

摘要

Road traffic crashes have been the leading cause of death among young people.Most of these accidents occur when the driver becomes distracted due to fatigueor external factors. Vehicle platooning systems such as Cooperative AdaptiveCruise Control (CACC) are one of the results of the effort devoted to thedevelopment of technologies for decreasing the number of road crashes andfatalities. Previous studies have suggested such systems improve up to 273\%highway traffic throughput and fuel consumption in more than 15\% if theclearance between vehicles in this class of roads can be reduced to 2 meters.This paper proposes an approach that guarantees a minimum safety distancebetween vehicles taking into account the overall system delays and brakingcapacity of each vehicle. A $l\infty$-norm Robust Model Predictive Controller(RMPC) is developed to guarantee the minimum safety distance is not violateddue to uncertainties on the lead vehicle behavior. A formulation for a lowerbound clearance of vehicles inside a platoon is also proposed. Simulationresults show the performance of the proposed approach compared to a nominalcontroller when the system is subject to both modeled and unmodeleddisturbances.