Deep neural network models have been widely used for environment perception of intelligent vehicles. However, due to models’ innate probabilistic property, the lack of transparency, and sensitivity to data, perception results have inevitable uncertainties. To compensate for the weakness of probabilistic models, many pieces of research have been proposed to analyze and quantify such uncertainties. For safety-critical intelligent vehicles, the uncertainty analysis of data and models for environment perception is especially important. Uncertainty estimation can be a way to quantify the risk of environment perception. In this regard, it is essential to deliver a comprehensive survey. This work presents a comprehensive overview of uncertainty estimation in deep neural networks for environment perception of intelligent vehicles. First, we provide a systematic and intuitive understanding of the classification and modeling of uncertainty and then summarize methods for uncertainty estimation in deep neural networks. Considering the research of epistemic uncertainty estimation as a study-worthy branch, the methods on epistemic uncertainty estimation are also illustrated in chronological order. Next, we present the application of uncertainty estimation in environment perception tasks including object detection, segmentation, trajectory prediction, depth estimation, optical flow, and so on. For these typical tasks, we make a detailed analysis in aspects of uncertainty type, baseline, or other features, in order to provide a macroscopical view of models. Finally, we give the outlooks for the uncertainty estimation in environment perception of intelligent vehicles in three aspects: epistemic uncertainty estimation with less computation, aleatoric uncertainty estimation with modified loss function, and uncertainty estimation for 3D perception based on LiDAR point cloud.