Internet of Vehicles (IoV) based on crowdsensing technology,which gets traffic data by smartphone or panel PC from ordinary person,has solved the problem that getting sufficient data at low cost.However,it also makes a new problem that the data quality of the system is deteriorated.To solve this problem,by analyzing the structure of crowdsensing data and the characteristics of abnormal data in crowdsensing IoV,a data detection algorithm is put forward to eliminate the abnormal data in IoV system and consequently improve data quality.In the algorithm,kernel density estimation theory is used to estimate the probability density of traffic data,and a belief function is then constructed to derive the confidence value of every detected data.According to the statistical theory,the data whose confidence value is less than 0 is regarded as abnormal data.Finally,the feasibility and performance of the presented algorithm are simulated.The results show that the proposed algorithm can meet practical demands and achieve better performance than that of traditional statistical detection methods.