How to ensure robust and accurate 3D object detection under various environment is essential for autonomous driving (AD) environment perception. While, until now, most of the existing 3D object detection methods are based on the ordinary driving scenes provided by the mainstream dataset. The researches on actual complex scenes (adverse illumination, inclement weather, distant or small objects, etc.) have been ignored and there is still no comprehensive review of the recent progress in this field. Thence, this paper aims to gain a deep insight on the performance and challenges of 3D object detection methods under complex scenes for AD. Firstly, we discuss the complex driving environments in actual and the perception limitations of mainstream sensors (LIDAR and camera). Then we analyze the performance and challenges of single-modality 3D object detection methods. Therefore, in order to improve the accuracy and robustness of 3D object detection methods in some complex AD scenes, the fusion of L-C (LIDAR-camera) is recommended and systematically analyzed. Finally, some suitable datasets and potential directions are comparatively summarized to support the relative research in complex driving scenes. We hope that this review could facilitate people's research and look forward to more progress in this timely and crucial problem field.

Visual odometry (VO) is a prevalent way to deal with the relative localization problem, which is becoming increasingly mature and accurate, but it tends to be fragile under challenging environments. Comparing with classical geometry-based methods, deep learning-based methods can automatically learn effective and robust representations, such as depth, optical flow, feature, ego-motion, etc., from data without explicit computation. Nevertheless, there still lacks a thorough review of the recent advances of deep learning-based VO (Deep VO). Therefore, this paper aims to gain a deep insight on how deep learning can profit and optimize the VO systems. We first screen out a number of qualifications including accuracy, efficiency, scalability, dynamicity, practicability, and extensibility, and employ them as the criteria. Then, using the offered criteria as the uniform measurements, we detailedly evaluate and discuss how deep learning improves the performance of VO from the aspects of depth estimation, feature extraction and matching, pose estimation. We also summarize the complicated and emerging areas of Deep VO, such as mobile robots, medical robots, augmented and virtual reality, etc. Through the literature decomposition, analysis, and comparison, we finally put forward a number of open issues and raise some future research directions in this field.

In the past decade, deep learning has been widely applied across various fields. However, its applicability in open-world scenarios is often limited due to the lack of quantifying uncertainty in both data and models. In recent years, a multitude of uncertainty quantification (UQ) approaches for neural networks have emerged and found applications in safety-critical domains such as autonomous vehicles and medical analysis. This paper aims to review the latest advancements in UQ methods and investigate their application specifically in the field of computer vision and autonomous vehicles. Initially, we identify several key qualifications, namely practicability, robustness, accuracy, scalability, and efficiency (referred to as PRASE), and employ them as evaluation criteria throughout this study. By considering these criteria as uniform measurements, we meticulously evaluate and compare the performance of different types of UQ methods, including Bayesian methods, ensemble methods, and single deterministic methods. Furthermore, we delve into the discussion of their application in diverse tasks within the autonomous vehicle domain, such as semantic segmentation, object detection, depth estimation, and end-to-end control. Through comprehensive analysis and comparison, we identify a range of challenges and propose future research directions in this field. Our findings shed light on the importance of addressing uncertainty quantification in deep learning models and provide insights into enhancing the reliability and performance of autonomous vehicles in real-world scenarios.

In the context of automated driving, navigating through challenging urban environments with dynamic objects, large-scale scenes, and varying lighting/weather conditions, achieving accurate localization is paramount for highly-automated (HAVs) or autonomous vehicles (AVs). An imprecise localization can greatly impact subsequent decision-making to manage an HAV or AV’s motion (planning and control tasks). In recent years, visual simultaneous localization and mapping (VSLAM) has shown substantial progress and equipping it can lead to handling non-standardized situations of real-world scenes and achieving higher localization and mapping accuracy. In this article, we present a comprehensive analysis of the current research status of VSLAM and its potential application to HAV or AV operating in complex urban environments. We first discuss the criteria to assess how well for the solutions that VSLAM methods offer to address the challenges, which include real-time performance, accuracy, robustness, and system operating cost. By employing these assessment criteria, we evaluate various VSLAM methods in four essential aspects including rejection and tracking of high dynamic objects, map construction in large-scale environments, loop detection and error correction, and sustainable operation and map updating. This evaluation provides valuable insights into the effectiveness of different VSLAM techniques. We then discuss potential research directions for leveraging VSLAM methods in achieving high-level automated driving in complex settings. We hope this article to serve as a timely update on recent progress and advances in VSLAM which are applicable to HAVs or AVs. To facilitate future research, we create a repository that includes links to relevant reviews and methodological papers for learning at https://github.com/bumblebee15138/VSLAM for HAVs and AVs.

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.