Analysis and prediction of unmanned driving control network based on vehicle flow and speed

Abstract

In this paper, we used the bimodal probability distribution function model and the joint relationship curve of vehicle flow and vehicle speed to study the correlation and statistical characteristics of the traffic flow and vehicle speed. We created a joint probability distribution model to reflect the nested relationship between traffic flow and speed. Through the determination of optimal parameters and fitting scheme, the autonomous control network gives an estimation control plan for the road traffic of the road section and formulates the optimized travel route. Then we showed through an example that when the vehicle speed is 40km/h and 50 km/h, the traffic volume is 2169.4 pcu/h and 1502.4 pcu/h, respectively. The speed of the vehicle gradually decreases with the increase of traffic volume. At the peak point, when the traffic congestion begins to appear, traffic volume and vehicle speed are (2532.65, 25.38), which is the saturated traffic volume, showing traffic demand is greater than the traffic capacity of the road section.