Cooperative adaptive cruise control applying stochastic linear model predictive control strategies

In this paper a cooperative adaptive cruise control approach using stochastic, linear model predictive control strategies is presented. The presented approach deals with an urban traffic environment where vehicle to vehicle and vehicle to infrastructure communication systems are available. The goal is the minimization of a vehicle's fuel consumption in a vehicle-following scenario. This is achieved by minimizing a piecewise linear approximation of the vehicle's fuel consumption map. By means of a conditional Gaussian model the probability distribution of the upcoming velocity of the preceding vehicle is estimated based on current measurements and upcoming traffic light signals. The predicted distribution function of the predecessor's velocity is used in two ways for stochastic model predictive control. On the one hand, individual chance constraints are introduced and subsequently reformulated to obtain an equivalent deterministic model predictive control problem. On the other hand, samples are drawn from the prediction model and used for a randomized optimization approach. Finally, the two developed stochastic control strategies are evaluated and compared against a deterministic model predictive control approach by means of a virtual traffic simulation. The evaluation of the controllers show a significant reduction of the fuel consumption compared to the predecessor while increasing safety and driving comfort.