Gradient-Based Distributed Cruise Control Under Intermittent V2V Communication for Smoothing Traffic Flow

We study the problem of smoothing traffic flow in a mixed traffic scenario during the cruising phase of vehicles. We consider a connected vehicle system (CVS) composed of multiple human driving vehicles (HDVs) and multiple autonomous vehicles (AVs). For the HDVs, the human driver behavior is modeled by the widely adopted optimal velocity model. The role of the AV is to guide the traffic flow through regulating its own motion based on available traffic information. To avoid network congestion caused by heavy network resource utilization, each AV intermittently communicates with other vehicles. The intermittent vehicle-to-vehicle communication mechanism (I2CM) is adopted to qualitatively reduce the communication resources occupation. The optimal design of the cruise control for the AVs under I2CM is formulated as an optimal state feedback control problem with a random sparse structure constraint (RSSC). We derive the first analytical expression for the gradient of the cost function with respect to the control law with RSSC. We develop an algorithm that distributively estimates the gradient based on available data. We further design a gradient-based distributed cruise control strategy for the smoothing traffic flow problem under I2CM. We conduct simulations on a CVS system comprising 20 vehicles to evaluate the effectiveness of the proposed cruise control strategy. The results reveal that, on average, each AV contributes to a 15% improvement in the driving smoothness of HDVs relative to the scenario without any AVs.