Vehicle localization is a critical component for autonomous driving, which estimates the position and orientation of vehicles. To achieve the goal of quick and accurate localization, we develop a system that can dynamically switch the features applied for localization. Specifically, we develop a feature based on convolutional neural network targeting at accurate matching, which proves high rotation invariant property that can help to overcome the relatively large error when vehicles turning at corners. However, when the vehicle motion mainly involves translation, we apply the ORB feature to localize the vehicle, as it demonstrates similar accuracy in translation estimation. Through dynamic switching between features, we can accurately localize the vehicle with high time performance. To filter out noise features, we train a CNN neural network to semantically understand the images and filter the features from moving objects and infinity position. During the pose estimation stage, we rely on the depth of the 3D points to identify the inliers satisfying RANSAC. Experiments demonstrate the superb performance of our method.