4.7 Article

3-D Object Detection for Multiframe 4-D Automotive Millimeter-Wave Radar Point Cloud

Related references

Note: Only part of the references are listed.
Article Robotics

Multi-Class Road User Detection With 3+1D Radar in the View-of-Delft Dataset

Andras Palffy et al.

Summary: In this study, a state-of-the-art object detector (PointPillars) is applied to 3+1D radar data. The benefits of additional elevation information, together with Doppler, radar cross section, and temporal accumulation, are explored in multi-class road user detection. Results show that object detection performance on 64-layer LiDAR data outperforms that on 3+1D radar data, but the gap can be reduced by adding elevation information and integrating successive radar scans. The VoD dataset, a valuable experimental dataset, is provided for scientific benchmarking.

IEEE ROBOTICS AND AUTOMATION LETTERS (2022)

Article Computer Science, Artificial Intelligence

Deep learning-based perception systems for autonomous driving: A comprehensive survey

Li-Hua Wen et al.

Summary: This paper provides a comprehensive survey of the recent progress in deep learning-based object detection tasks in autonomous driving, including 3-D object detection, road detection, traffic sign detection, and traffic light detection and classification. The research findings are of great significance for advancing unmanned driving technology and inspiring future research.

NEUROCOMPUTING (2022)

Proceedings Paper Computer Science, Artificial Intelligence

TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving

Lianqing Zheng et al.

Summary: This paper introduces a dataset named TJ4DRadSet, which contains 4D radar point clouds for autonomous driving research, and provides a 4D radar-based 3D object detection baseline for the dataset.

2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) (2022)

Article Engineering, Electrical & Electronic

Milli-RIO: Ego-Motion Estimation With Low-Cost Millimetre-Wave Radar

Yasin Almalioglu et al.

Summary: This paper introduces a solution based on millimeter-wave radar and inertial measurement unit sensor for estimating six-degrees-of-freedom ego-motion of a moving radar indoors, achieving precision on the order of a few centimeters.

IEEE SENSORS JOURNAL (2021)

Review Engineering, Electrical & Electronic

Sensing system of environmental perception technologies for driverless vehicle: A review of state of the art and challenges

Qiping Chen et al.

Summary: Environmental perception technology is crucial for the safety of driverless vehicles, but it is facing new challenges in the new era. This review paper summarizes the advantages, disadvantages, and applicable occasions of commonly used sensing methods, and discusses the new challenges in technology, external environment, and applications. Additionally, it highlights the future development trends and efforts in environmental perception technology.

SENSORS AND ACTUATORS A-PHYSICAL (2021)

Article Computer Science, Artificial Intelligence

Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy

Duarte Fernandes et al.

Summary: Autonomous vehicles are relying on deep learning and 3D scanners for better perception and object detection; since 2010, there has been a significant increase in the number and innovation of methods for self-driving systems; there is notable development in newer techniques adapted to LiDAR data.

INFORMATION FUSION (2021)

Proceedings Paper Computer Science, Artificial Intelligence

RPFA-Net: a 4D RaDAR Pillar Feature Attention Network for 3D Object Detection

Baowei Xu et al.

Summary: In this study, a novel approach named RPFA-Net is proposed, utilizing a 4D RaDAR sensor and a self-attention mechanism to enhance the ability of regressing object heading angles and improving detection accuracy. Compared with existing methods, RPFA-Net achieves an increase of 8.13% in 3D mAP and 5.52% in BEV mAP, outperforming state-of-the-art 3D detection methods on the Astyx HiRes 2019 dataset.

2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) (2021)

Article Computer Science, Information Systems

Semantic Segmentation on 3D Occupancy Grids for Automotive Radar

Robert Prophet et al.

IEEE ACCESS (2020)

Proceedings Paper Computer Science, Artificial Intelligence

STD: Sparse-to-Dense 3D Object Detector for Point Cloud

Zetong Yang et al.

2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) (2019)

Proceedings Paper Automation & Control Systems

Point Cloud Compression for 3D LiDAR Sensor using Recurrent Neural Network with Residual Blocks

Chenxi Tu et al.

2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) (2019)

Article Chemistry, Analytical

SECOND: Sparsely Embedded Convolutional Detection

Yan Yan et al.

SENSORS (2018)