## 1 Introduction  
With the rapid development of national economy and the continuous improvement of living standards, the popularization of automobiles becomes an inevitable trend. With the rapid growth of motor vehicles and traffic flow, urban traffic is facing enormous pressure [1]. According to the statistics, the growth rate of the total mileage of a highway is 2.5%, while the annual growth rate of vehicles is more than 10%. The growth rate of vehicles is far beyond the growth rate of roads [2]. The traffic congestion, traffic accidents, and traffic environment restrict the construction and development of economy, so it has become a common problem faced by all countries. The developed countries and developing countries are suffering from worsening traffic issues [3]. Therefore, we have to consider new ideas to solve the traffic problem. Traditionally, the way to solve the traffic problem is to solve the contradiction between vehicle and road. There are two common ways: The first is to control demand, and the most direct way is to limit the increase of vehicles. The second is to increase supply [4]. But these methods have their limitations, so they are not good methods in the current condition. It is necessary to find other methods to meet the increasing traffic demand. Meanwhile, the transportation is a complex and comprehensive system, and it is difficult to solve the traffic problems only from the perspective of the road or vehicle. In order to solve the traffic problems, some traffic flow parameter detection methods are proposed. In Reference [5], an improved recursive wavelet transform-based algorithm is proposed to detect traffic flow outliers and changes. Firstly, according to the 3-sigma principle, the monitoring threshold method was set to realize the real-time warning of traffic flow status. Secondly, by using the improved recursive wavelet transform statistics, combined with the wavelet composite information, the characteristic frequency and the optimal search length of the wavelet transform were selected to detect and estimate the abnormal points and change points of traffic flow quickly, so as to realize the on-line monitoring of traffic flow status. The simulation results show that this method can detect the traffic flow parameters, but it has the disadvantage of poor anti-noise performance. In Reference [6], a method of stray current distribution and monitoring in urban rail transit depot is proposed. The parameters related to stray current in the depot were tested onsite. It was found that a large amount of stray current in the ground entered the depot track during the operation of the line and flowed to the main line through the one-way conduction device. The current, rail ground potential, soil potential gradient, and stray current flowing through the one-way conduction device were analyzed based on the parameters, such as the stray current direction, the distribution law of stray current in the depot, and the relevant parameters of traffic flow, which were are obtained to realize the monitoring. Experimental results show that this method can completely extract moving objects in traffic flow, but it is easy for it to be infected by the environment and has low denoising coefficient. In Reference [7], the optimization method of urban traffic flow prediction and signal control is proposed. The number of vehicles in the next detection area was predicted according to the model of joint driving and lane changing. The traffic action of the intersection was then modeled by graph theory. On this basis, an efficient vehicle scheduling algorithm based on a graph was designed to realize the detection of traffic flow parameters. The results show that the method was easily interfered by light and the application effect was not good. Therefore, an autonomous detection method for traffic flow parameters of in-vehicle sensing equipment based on symmetrical difference is put forward.