Over the last decades,deep neural networks have been penetrated into all fields of science and the real world. As a result of the lack of quantifiable data and model uncertaint, deep learning is frequently brittle,illogical, and challenging to provide trustworthy assurance for autonomous vehicles(AV) perception.This hole is filled by the suggested approach to uncertainty quantification. Nevertheless, most of the previous studies focused on the methodology and there is still a lack of research on the application of AV. To our knowledge, this paper is the first time to review the application of uncertainty in the field of AV perception and localization. In the first place, this paper analyzes the sources of uncertainty in autonomous perception,including the uncertainty brought on by sensor internal and external factors as well as the sensor distortion caused on by complex scenes. In the second place,we propose an evaluation criterion and use the criterion to carry out a quantitative analysis of perception field of application for autonomous vehicles,and we discuss the mainstream datasets. Thirdly, we put forward a number of open issues and raise some future research directions, which is of guiding significance to readers who are beginning to enter this field.We believe that epistemic uncertainty is currently the dominant research direction and that there is still a long way to go in the study of aleatoric uncertainty.And our paper will be devoted to promoting the development of uncertainty research of AV perception.

We present a novel low-cost visual odometry method of estimating the ego-motion (self-motion) for ground vehicles by detecting the changes that motion induces on the images. Different from traditional localization methods that use differential global positioning system (GPS), precise inertial measurement unit (IMU) or 3D Lidar, the proposed method only leverage data from inexpensive visual sensors of forward and backward onboard cameras. Starting with the spatial-temporal synchronization, the scale factor of backward monocular visual odometry was estimated based on the MSE optimization method in a sliding window. Then, in trajectory estimation, an improved two-layers Kalman filter was proposed including orientation fusion and position fusion. Where, in the orientation fusion step, we utilized the trajectory error space represented by unit quaternion as the state of the filter. The resulting system enables high-accuracy, low-cost ego-pose estimation, along with providing robustness capability of handing camera module degradation by automatic reduce the confidence of failed sensor in the fusion pipeline. Therefore, it can operate in the presence of complex and highly dynamic motion such as enter-in-and-out tunnel entrance, texture-less, illumination change environments, bumpy road and even one of the cameras fails. The experiments carried out in this paper have proved that our algorithm can achieve the best performance on evaluation indexes of average in distance (AED), average in X direction (AEX), average in Y direction (AEY), and root mean square error (RMSE) compared to other state-of-the-art algorithms, which indicates that the output results of our approach is superior to other methods.