Target recognition is an important technology for intelligent cars to perceive the surrounding environment to achieve smart driving

high-precision object recognition algorithms can provide a guarantee for the safe driving of intelligent cars. A vehicle recognition algorithm based on multi-feature extraction of 3 D Li DAR point cloud is proposed. It combines the extracted 12-dimensional features of Li DAR data with a classifier to identify vehicle objects on urban roads. Firstly

it clusters non-ground Li DAR point clouds and extracts 12-dimensional features from each cluster of point cloud data

then four classifiers are trained according to these 12 dimensional features. Finally

it uses the KITTI data set for simulation

comparing the performance of four classifiers and the accuracy of identifying vehicle objects in three different dimensional features（ 12-dimensional

26-dimensional

and8-dimensional features）. The simulation results show the 12-dimensional features extracted in this paper compared with the other two dimensions of Li DAR features can improve the accuracy of vehicle object classification

and the recognition accuracy combined with random forest classifier is superior to other classifier methods. In addition

the algorithm can meet the accuracy of vehicle recognition in the road scene of Baidu Apollo dataset.