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3D Object Detection from Images for Autonomous Driving: A Survey [article]

Xinzhu Ma, Wanli Ouyang, Andrea Simonelli, Elisa Ricci
2022 arXiv   pre-print
3D object detection from images, one of the fundamental and challenging problems in autonomous driving, has received increasing attention from both industry and academia in recent years.  ...  Benefiting from the rapid development of deep learning technologies, image-based 3D detection has achieved remarkable progress.  ...  CONCLUSIONS This paper provides a comprehensive survey of the recent developments in image-based 3D detection for autonomous driving.  ... 
arXiv:2202.02980v2 fatcat:2hela3uz2bgmtgyzpw5sqslqla

3D Object Detection for Autonomous Driving: A Survey [article]

Rui Qian, Xin Lai, Xirong Li
2022 arXiv   pre-print
Despite existing efforts, 3D object detection for autonomous driving is still in its infancy. Recently, a large body of literature have been investigated to address this 3D vision task.  ...  To this end, 3D object detection serves as the core basis of perception stack especially for the sake of path planning, motion prediction, and collision avoidance etc.  ...  This survey paper will take a structured glance at 3D object detection, one of the core techniques for autonomous driving. Perception in 3D space is a prerequisite in autonomous driving.  ... 
arXiv:2106.10823v2 fatcat:z2lytpximjeyzmnrtlltklvk5a

Self-Driving Cars: A Survey [article]

Claudine Badue, Rânik Guidolini, Raphael Vivacqua Carneiro, Pedro Azevedo, Vinicius Brito Cardoso, Avelino Forechi, Luan Jesus, Rodrigo Berriel, Thiago Paixão, Filipe Mutz, Lucas Veronese, Thiago Oliveira-Santos (+1 others)
2019 arXiv   pre-print
We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized  ...  Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Esp\'irito Santo (UFES), named Intelligent Autonomous  ...  We survey the literature on methods for moving objects detection and tracking in the context of self-driving cars in Section 3.4.  ... 
arXiv:1901.04407v2 fatcat:uwrgi5wjlbckdhtyy4eelinmde

Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies [article]

Yu Huang, Yue Chen
2020 arXiv   pre-print
This is a survey of autonomous driving technologies with deep learning methods.  ...  Due to the limited space, we focus the analysis on several key areas, i.e. 2D and 3D object detection in perception, depth estimation from cameras, multiple sensor fusion on the data, feature and task  ...  Two survey papers [124] [125] focus on 3-D point clouds for 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation.  ... 
arXiv:2006.06091v3 fatcat:nhdgivmtrzcarp463xzqvnxlwq

Multi-modal Sensor Fusion for Auto Driving Perception: A Survey [article]

Keli Huang, Botian Shi, Xiang Li, Xin Li, Siyuan Huang, Yikang Li
2022 arXiv   pre-print
Multi-modal fusion is a fundamental task for the perception of an autonomous driving system, which has recently intrigued many researchers.  ...  In this paper, we provide a literature review of the existing multi-modal-based methods for perception tasks in autonomous driving.  ...  KITTI [26] open benchmark dataset, as one of the most used dataset for object detection in autonomous driving, contains 2D, 3D, and bird's eye view detection tasks.  ... 
arXiv:2202.02703v2 fatcat:skrohglkvjavtavlwo3mavnveq

Multi-Modal 3D Object Detection in Autonomous Driving: a Survey [article]

Yingjie Wang, Qiuyu Mao, Hanqi Zhu, Yu Zhang, Jianmin Ji, Yanyong Zhang
2021 arXiv   pre-print
In this survey, we first introduce the background of popular sensors for autonomous cars, including their common data representations as well as object detection networks developed for each type of sensor  ...  Next, we discuss some popular datasets for multi-modal 3D object detection, with a special focus on the sensor data included in each dataset.  ...  In this part, we discuss some widely used datasets for 3D object detection in autonomous driving.  ... 
arXiv:2106.12735v2 fatcat:5twzbk4yhrcfzddp7zghnsivna

Anomaly Detection in Autonomous Driving: A Survey [article]

Daniel Bogdoll, Maximilian Nitsche, J. Marius Zöllner
2022 arXiv   pre-print
This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data.  ...  We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes.  ...  Acknowledgment This work results from the project KI Data Tooling (19A20001J), funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK).  ... 
arXiv:2204.07974v1 fatcat:3rdola4tjfesllr6dqhqgf3tve

A survey of deep learning techniques for autonomous driving

Sorin Grigorescu, Bogdan Trasnea, Tiberiu Cocias, Gigel Macesanu
2019 Journal of Field Robotics  
The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving.  ...  These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration and motion control algorithms.  ...  Acknowledgment The authors would like to thank Elektrobit Automotive for the infrastructure and research support.  ... 
doi:10.1002/rob.21918 fatcat:pjyk4lwjavf63jz4pmc3mnuqe4

A Survey of Autonomous Driving: Common Practices and Emerging Technologies [article]

Ekim Yurtsever, Jacob Lambert, Alexander Carballo, Kazuya Takeda
2020 arXiv   pre-print
This paper discusses unsolved problems and surveys the technical aspect of automated driving.  ...  Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise.  ...  (d) The 3D Lidar data with object detection results from SECOND [126] .  ... 
arXiv:1906.05113v2 fatcat:2hqztllrgjhndbc5aebduvukai

A Survey on Safety-Critical Driving Scenario Generation – A Methodological Perspective [article]

Wenhao Ding, Chejian Xu, Mansur Arief, Haohong Lin, Bo Li, Ding Zhao
2022 arXiv   pre-print
In this survey, we focus on the algorithms of safety-critical scenario generation in autonomous driving.  ...  Autonomous driving systems have witnessed a significant development during the past years thanks to the advance in machine learning-enabled sensing and decision-making algorithms.  ...  Many tasks in autonomous driving, such as 3D object detection and 3D segmentation, require 3D annotations.  ... 
arXiv:2202.02215v4 fatcat:uxcvqxk6qna5jh53dwdwqanc4q

Autonomous Driving in Adverse Weather Conditions: A Survey [article]

Yuxiao Zhang, Alexander Carballo, Hanting Yang, Kazuya Takeda
2021 arXiv   pre-print
However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time.  ...  Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences.  ...  In general, the model ination from 3D point clouds after detection.  ... 
arXiv:2112.08936v1 fatcat:hmgjhywy7rgx3fgrk6yxnu56ie

Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses [article]

Yao Deng, Tiehua Zhang, Guannan Lou, Xi Zheng, Jiong Jin, Qing-Long Han
2021 arXiv   pre-print
This survey provides a thorough analysis of different attacks that may jeopardize ADSs, as well as the corresponding state-of-the-art defense mechanisms.  ...  event, spanning from anti-fatigue safe driving to intelligent route planning.  ...  PointRCNN [34] adapts the architecture of RCNN to take 3D point cloud as input for object detection and achieves a superior performance. 3) Semantic segmentation: Semantic segmentation in autonomous  ... 
arXiv:2104.01789v2 fatcat:zekeddt7zzcnrphu3f4yw6vzii

A Survey on 3D Object Detection Methods for Autonomous Driving Applications

Eduardo Arnold, Omar Y. Al-Jarrah, Mehrdad Dianati, Saber Fallah, David Oxtoby, Alex Mouzakitis
2019 IEEE transactions on intelligent transportation systems (Print)  
To the best of our knowledge this is the first survey on 3D object detection methods used for autonomous driving applications.  ...  Alternatively, 3D object detection methods introduce a third dimension that reveals more detailed object's size and location information.  ...  for autonomous driving vehicles • comparing 3D object detection methods performances on a baseline benchmark • identifying research gaps and future research directions.  ... 
doi:10.1109/tits.2019.2892405 fatcat:dwqhqd7mwfcq3cugefxhgqx7b4

Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Survey [article]

Zhengwei Bai, Guoyuan Wu, Xuewei Qi, Yongkang Liu, Kentaro Oguchi, Matthew J. Barth
2022 arXiv   pre-print
Object detection plays a fundamental role in enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing safety, mobility, and sustainability issues of  ...  In this paper, we review the research progress for infrastructure-based object detection and tracking systems.  ...  The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein.  ... 
arXiv:2201.11871v2 fatcat:jyopndlcevcx5eym4e4trrjhwu

A Survey of End-to-End Driving: Architectures and Training Methods [article]

Ardi Tampuu, Maksym Semikin, Naveed Muhammad, Dmytro Fishman and Tambet Matiisen
2020 arXiv   pre-print
In this paper we take a deeper look on the so called end-to-end approaches for autonomous driving, where the entire driving pipeline is replaced with a single neural network.  ...  Autonomous driving is of great interest to industry and academia alike. The use of machine learning approaches for autonomous driving has long been studied, but mostly in the context of perception.  ...  ACKNOWLEDGMENTS The authors would like to thank Hannes Liik for fruitful discussions.  ... 
arXiv:2003.06404v1 fatcat:ekb4g7waa5fyldfaxhgnb3a5xm
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