In order to realize high-precision sensor sensing system for unmanned vehicle, a method based on D–S (Dempster–Shafer) evidence theory was presented. D–S evidence theory is a method of evidence processing. However, because of the limitation of multiplication rule, it cannot deal with the evidence of high conflict caused by the failure of a sensor in the system. In the existing D–S theory framework, the prior probability is used to measure the sensor’s failure rate. The correlation matrix is obtained by the mass distribution of evidence, and the interval classification is carried out accordingly. Bayes formula is used to adjust the prior probability of the sensor by synthesizing the risk distribution function of different intervals, and the calculation rule of correction report using convolution rule is proposed. Compared to existing methods, it can reduce the conflict degree and information entropy of the system, so that a reasonable decision can be made.

为了实现无人驾驶车辆的高精度传感器传感系统，提出了一种基于DS（Dempster-Shafer）证据理论的方法。D–S证据理论是证据处理的一种方法。但是，由于乘法规则的限制，它不能处理由系统中的传感器故障引起的高冲突的证据。在现有的D–S理论框架中，先验概率用于测量传感器的故障率。通过证据的质量分布获得相关矩阵，并据此进行区间分类。利用贝叶斯公式，通过综合不同区间的风险分布函数，调整传感器的先验概率，并提出了利用卷积法则的修正报告计算规则。与现有方法相比，