Journal Article10.1109/JSTSP.2021.3058062
Learning-Based Extended Object Tracking Using Hierarchical Truncation Measurement Model With Automotive Radar
Yuxuan Xia,Pu Wang,Karl Berntorp,Lennart Svensson,Karl Granstrom,Hassan Mansour,Petros T. Boufounos,Philip Orlik +7 more
38
TL;DR: This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar that is modeled as a hierarchical truncated Gaussian (HTG) with structural geometry parameters that can be learned from the training data.
read more
Abstract: This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian (HTG) with structural geometry parameters that can be learned from the training data. The HTG measurement model provides an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Moreover, large-scale radar datasets can be leveraged to learn the geometry-related model parameters and offload the computationally demanding model parameter estimation from the state update step. The learned HTG measurement model is further incorporated into a random matrix based EOT approach with two (multi-sensor) measurement updates: one is based on a factorized Gaussian inverse-Wishart density representation and the other is based on a Rao-Blackwellized particle density representation. The effectiveness of the proposed approaches is verified on both synthetic data and real-world nuScenes dataset over 300 trajectories.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
TL;DR: A big picture of the deep radar perception stack is provided, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion.
Joint MIMO Precoding and Computation Resource Allocation for Dual-Function Radar and Communication Systems With Mobile Edge Computing
TL;DR: An integrated communication, radar sensing, and mobile-edge computing (CRMEC) architecture is developed, where user terminals perform radar sensing and computation offloading simultaneously at the same spectrum by using multiple-input and multiple-output MIMO arrays and dual-function radar-communication techniques.
68
Sensing and Machine Learning for Automotive Perception: A Review
01 Jun 2023
TL;DR: In this paper , the authors provide an overview of different sensor modalities like cameras, radars, and LiDARs used commonly for perception, along with the associated data processing techniques.
49
Sensing and Machine Learning for Automotive Perception: A Review
Ashish Pandharipande,Chih-Hong Cheng,Justin Dauwels,Sevgi Zubeyde Gurbuz,Guofa Li,A. Piazzoni,Pu Wang,Avik Santra +7 more
TL;DR: An overview of different sensor modalities, such as cameras, radars, and light detection and ranging (LiDAR) used commonly for perception, along with the associated data processing techniques is provided in this article .
38
Exploiting Temporal Relations on Radar Perception for Autonomous Driving
01 Jun 2022
TL;DR: In this paper , the authors exploit the temporal information from successive ego-centric bird-eye-view radar image frames for radar object recognition and propose a temporal relational layer to explicitly model the relations between objects within successive radar images.
33
References
•Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
•Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
28.9K
nuScenes: A Multimodal Dataset for Autonomous Driving
Holger Caesar,Varun Bankiti,Alex H. Lang,Sourabh Vora,Venice Erin Liong,Qiang Xu,Anush Krishnan,Yu Pan,Giancarlo Baldan,Oscar Beijbom +9 more
- 14 Jun 2020
TL;DR: nuScenes as discussed by the authors is the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view.
•Posted Content
nuScenes: A multimodal dataset for autonomous driving
Holger Caesar,Varun Bankiti,Alex H. Lang,Sourabh Vora,Venice Erin Liong,Qiang Xu,Anush Krishnan,Yu Pan,Giancarlo Baldan,Oscar Beijbom +9 more
TL;DR: nuScenes as mentioned in this paper is the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view.
3.7K
Survey of maneuvering target tracking. Part I. Dynamic models
X. Rong Li,Vesselin P. Jilkov +1 more
TL;DR: A comprehensive and up-to-date survey of the techniques for tracking maneuvering targets without addressing the measurement-origin uncertainty is presented in this article, including 2D and 3D maneuver models as well as coordinate-uncoupled generic models for target motion.
2.3K