442 research outputs found
Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges
Radar is a key component of the suite of perception sensors used for safe and
reliable navigation of autonomous vehicles. Its unique capabilities include
high-resolution velocity imaging, detection of agents in occlusion and over
long ranges, and robust performance in adverse weather conditions. However, the
usage of radar data presents some challenges: it is characterized by low
resolution, sparsity, clutter, high uncertainty, and lack of good datasets.
These challenges have limited radar deep learning research. As a result,
current radar models are often influenced by lidar and vision models, which are
focused on optical features that are relatively weak in radar data, thus
resulting in under-utilization of radar's capabilities and diminishing its
contribution to autonomous perception. This review seeks to encourage further
deep learning research on autonomous radar data by 1) identifying key research
themes, and 2) offering a comprehensive overview of current opportunities and
challenges in the field. Topics covered include early and late fusion,
occupancy flow estimation, uncertainty modeling, and multipath detection. The
paper also discusses radar fundamentals and data representation, presents a
curated list of recent radar datasets, and reviews state-of-the-art lidar and
vision models relevant for radar research. For a summary of the paper and more
results, visit the website: autonomous-radars.github.io
Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis
The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders
Current perception models in autonomous driving heavily rely on large-scale
labelled 3D data, which is both costly and time-consuming to annotate. This
work proposes a solution to reduce the dependence on labelled 3D training data
by leveraging pre-training on large-scale unlabeled outdoor LiDAR point clouds
using masked autoencoders (MAE). While existing masked point autoencoding
methods mainly focus on small-scale indoor point clouds or pillar-based
large-scale outdoor LiDAR data, our approach introduces a new self-supervised
masked occupancy pre-training method called Occupancy-MAE, specifically
designed for voxel-based large-scale outdoor LiDAR point clouds. Occupancy-MAE
takes advantage of the gradually sparse voxel occupancy structure of outdoor
LiDAR point clouds and incorporates a range-aware random masking strategy and a
pretext task of occupancy prediction. By randomly masking voxels based on their
distance to the LiDAR and predicting the masked occupancy structure of the
entire 3D surrounding scene, Occupancy-MAE encourages the extraction of
high-level semantic information to reconstruct the masked voxel using only a
small number of visible voxels. Extensive experiments demonstrate the
effectiveness of Occupancy-MAE across several downstream tasks. For 3D object
detection, Occupancy-MAE reduces the labelled data required for car detection
on the KITTI dataset by half and improves small object detection by
approximately 2% in AP on the Waymo dataset. For 3D semantic segmentation,
Occupancy-MAE outperforms training from scratch by around 2% in mIoU. For
multi-object tracking, Occupancy-MAE enhances training from scratch by
approximately 1% in terms of AMOTA and AMOTP. Codes are publicly available at
https://github.com/chaytonmin/Occupancy-MAE.Comment: Accepted by TI
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