Abstract:

In the V2V environment of the internet of vehicles， the real-time movement status， driving conditions and road environment of the vehicle and its surrounding vehicles can be collected so as to provide more accurate information for vehicle adaptive cruise control （ACC） system. In order to eliminate the interference of headway fluctuation on longitudinal ACC under the mixed driving conditions of autonomous vehicles （AV） and manual vehicles （MV） ， a V2V based cooperative adaptive control method is proposed. Firstly， through V2V of the internet of Vehicles， the basic safety message （BSM） of the vehicle in the process of vehicle following is collected in real time so as to obtain the relative motion state and driving behavior sequence of the vehicles. Then， the linear quadratic regulator （LQR） is applied to infer the sequential driving maneuvers and short-term predict the vehicle motion state under the disturbance of traffic vehicle acceleration and deceleration. On this basis， a rolling optimization model and a cooperative adaptive control method for vehicle following distance are established to acquire the optimal equilibrium state of the headway of the mixed traffic flow. The test results show that under the acceleration/deceleration driving conditions of the preceding vehicle， the improved vehicle controller can respond faster to the change of the moving state of the preceding vehicle， and reduces the headway while ensuring the safe car-following distance and high traffic capacity.

Key words: adaptive cruise control, mixed heterogeneous fleet, cooperative control, model predictive control