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I Am back..😉😉🤘
I'll tell you some knowledge shear about Research Papers on Autonomous Vehicles and Computer vision
These things all about Self-Driving Cars and computer Vision 🚨🚨
I think you're also interested & enthusiastic like me
what is Research Papers & how do use them in AI?
A research paper is a document that reports the findings of original research. It is typically written by a researcher or group of researchers and submitted to a journal or conference for publication. Research papers are an important way for researchers to share their work with the wider community and to contribute to the advancement of knowledge.
In the field of artificial intelligence (AI), research papers are used to describe new algorithms, methods, and systems. They also discuss the results of experiments that have been conducted to evaluate the effectiveness of these new approaches. Research papers in AI are typically published in academic journals or conference proceedings.
Here are some of the ways that research papers are used in AI:
To learn about new techniques and methods: Research papers are a great way to learn about the latest advances in AI. By reading research papers, you can stay up-to-date on the latest research and find new ideas for your own work.
To get feedback on your work: If you are working on a new AI project, you can submit a research paper to a journal or conference for review. This is a great way to get feedback from other researchers and improve your work.
To build new AI systems: Research papers can be used to build new AI systems. By following the instructions in a research paper, you can implement a new algorithm or method in your own code.
To evaluate the effectiveness of AI systems: Research papers often report the results of experiments that have been conducted to evaluate the effectiveness of new AI systems. This information can be used to compare different approaches and to choose the best approach for a particular application.
If you are interested in learning more about AI, I encourage you to read research papers. They are a valuable resource for anyone who wants to stay up-to-date on the latest advances in this field.
Here are some tips for reading AI research papers:
Start with the abstract. The abstract will give you a brief overview of the paper and its main findings.
Read the introduction. The introduction will provide more background information on the topic of the paper and its significance.
Read the methods section. The methods section will describe how the research was conducted.
Read the results section. The results section will present the findings of the research.
Read the discussion section. The discussion section will interpret the results and discuss their implications.
If you don't understand something, don't be afraid to ask for help. There are many resources available to help you understand AI research papers, such as online tutorials and forums like (GitHub; Reddit; LinkedIn groups......etc! )
The following research papers Help me to gain more knowledge about CV & AV
I hope you too also
Object Detection
Object Tracking
Semantic Segmentation
Optimization
Multi-Task Learning
DEEP LEARNING
1- Deep Learning for LiDAR Point Cloud Classification in Remote Sensing: Link
2- A Survey of the Recent Architectures of Deep Convolutional Neural Networks: Link
3- Deep Learning on Point Clouds and Its Application:A Survey: Link
4- A survey on deep learning approaches for data integration in autonomous driving systems: Link
5- A Survey of Deep Learning Applications to Autonomous Vehicle Control: Link
6- Deep Learning on Point Clouds and Its Application: A Survey: Link
7- Data generation using simulation technology to improve perception mechanism of autonomous vehicles: Link
Reinforcement Learning
4- LEARNING TO MODULATE PRE-TRAINED MODELS IN RL: Link
Sensor Fusion
1- Radar and Vision Sensor Fusion for Vehicle Tracking: Link
2- Automatic Online Calibration Between Lidar and Camera: Link
3- Sensor Fusion Techniques for Autonomous Driving Applications: Link
4- Multi-modal sensor fusion towards three-dimensional airborne sonar imaging in hydrodynamic conditions: Link
5- Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges: Link
END-TO-END APPROACH
1- End-to-End Learning for Self-Driving Cars: Link
2- DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving: Link
3- ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning: Link
4- awesome-end-to-end-autonomous-driving: Link
5- End-to-end-Autonomous-Driving: Link
6- Incremental End-to-End Learning for Lateral Control in Autonomous Driving: Link
7- End-to-end autonomous vehicle lateral control with deep learning: Link
11- Longitudinal and lateral control of autonomous vehicles in multi-vehicle driving environments: Link
12- Multimodal End-to-End Autonomous Driving: Link
13- Variational End-to-End Navigation and Localization: Link
14- Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios: Link
15- Exploration of reinforcement learning algorithms for autonomous vehicle visual perception and control (best one ever): Link
16- Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey: Link
3D Reconstruction
1- High-quality 3D Reconstruction from Low-Cost RGB-D Sensors: Link
2- 3D Reconstruction using a Sparse Laser Scanner and a Single Camera for Outdoor Autonomous Vehicle: Link
3- 3D Scene Reconstruction and Completion for Autonomous Driving: Link
4- Omnidirectional 3D Reconstruction in Augmented Manhattan Worlds: Link
Maths
1- A Survey of Deep Learning for Mathematical Reasoning: Link
2- Deep Learning for Mathematical Reasoning (DL4MATH): Link
3- Linear algebra with transformers: Link
Survey Papers
1- A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services: Link
2- A Survey on Datasets for Decision-making of Autonomous Vehicle: Link
3- A survey on deep learning approaches for data integration in autonomous driving systems: Link
4- Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles: Link
5- Transformer-based models and hardware acceleration analysis in autonomous driving A survey: Link
6- How Simulation Helps Autonomous Driving: A Survey of Sim2real, Digital Twins,and Parallel Intelligence: Link
7- Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors: Link
8- Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches: Link
9- Machine Learning for Autonomous Vehicle’s Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions: Link
10- Autonomous Vehicles in 5G and Beyond: A Survey: Link
11- YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors: Link
15- A Survey of Deep RL and IL for Autonomous Driving Policy Learning: Link
16- A Survey of End-to-End Driving: Architectures and Training Methods: Link
17- Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art: Link
18- Multi-modal Sensor Fusion for Auto Driving Perception: A Survey: Link
19- A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles: Link
20- A Survey on Datasets for Decision-making of Autonomous Vehicle: Link
21- Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning: Link
22- Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches: Link
23- A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation: Link
Ensemble Learning
20- Ensemble Reinforcement Learning: A Survey: Link
21- Ensemble Methods for Object Detection: Link
Transformers:
22- Transformer-based models and hardware acceleration analysis in autonomous driving A survey Link
23- Planning-oriented Autonomous Driving: Link
24- A Survey of Deep Learning Applications to Autonomous Vehicle Control: Link
25- TRANSFORMER-BASED SENSOR FUSION FOR AUTONOMOUS DRIVING: A SURVEY: Link
V2X
Trajectory Generation Network
VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban Environments: Link
SLAM
Simultaneous Localisation and Mapping (SLAM): Part II State of the Art: Link
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers: Link
SLAM and data fusion for autonomous vehicles: from classical approaches to deep learning methods: Link
A Survey on Deep Learning for Localization and Mapping:Towards the Age of Spatial Machine Intelligence: Link
Comparison of modern open-source Visual SLAM approaches: Link
GPS
Sensor Fusion and Calibration of Inertial Sensors, Vision, Ultra-Wideband and GPS: Link
Algorithms for Autonomous Personal Navigation Systems: Link
Optical Flow
Planning
Design Space of Behaviour Planning for Autonomous Driving: Link
Behavior Modeling for Autonomous Driving: Link
A Survey of Path Planning Algorithms for Autonomous Vehicles: Link
Behavior Planning for Autonomous Driving: Methodologies, Applications, and Future Orientation: Link
Real-time Behaviour Planning Concept for Autonomous Vehicles: Link
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives: Link
Overview of Tools Supporting Planning for Automated Driving: Link
HD MAPS
Metric Learning
OTHERS
1- Optimization of deep neural networks: a survey and unified taxonomy: Link
2- A SURVEY OF PERFORMANCE OPTIMIZATION IN NEURAL NETWORK-BASED VIDEO ANALYTICS SYSTEMS: Link
3- A Survey on the Optimization of Neural Network Accelerators for Micro-AI On-Device Inference: Link
4- IF YOU WANT TO LEARN FILL INFORMATION OF CNN: Link
5- Artificial Neural Network Hyperparameters Optimization: A Survey Link
6- Implementing a Cloud Platform for Autonomous Driving: Link
7- An Overview of Lidar Imaging Systems for Autonomous Vehicles: Link
9- GG-Net: Gaze Guided Network for Self-driving Cars: Link
10- A Study on Multi-sensor Data Fusion Algorithm: Link
11- Analysis of Failures and Risks in Deep Learning Model Converters: A Case Study in the ONNX Ecosystem: Link
12- Follow Anything: Open-set detection, tracking, and following in real-time: Link
13- AIRBORNE ULTRASONIC IMAGING: SONAR-BASED IMAGE GENERATION FOR AUTONOMOUS VEHICLES: Link
14- The Deep Learning Compiler: A Comprehensive Survey: Link
15- Edge Computing for Autonomous Driving: Opportunities and Challenges Link
16- Deep Learning vs. Traditional Computer Vision Link
17- Computational models of object motion detectors accelerated using FPGA technology: Link
18- A Review and Comparative Study of Close-Range Geometric Camera Calibration Tools: Link
19- Survey of Machine Learning Accelerators: Link
20- Survey on Artificial Intelligence Approaches for Data Visualization: Link
21- WiROS: WiFi sensing toolbox for robotics: Link
22- Enabling Deep Learning on EdgeDevices: Link
23- Machine Learning at the Network Edge: A Survey: Link
24- THE NEURAL NETWORK ZOO: Link
TOOLS?
Useful Chrome Extensions for the Development of AI?
i) - Catalyzex
ii) - Paper-With-Video
If you have any best Tools or extensions chat with me....................
How to download free research papers; On SCIHUB website; Enter the DOI number of the paper
How to Write a Research Paper?
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How to submit research papers?
in 1- arXiV (it is a repository of preprints; basically researchers put their papers before they send them into the journals; this way they have the copyright to their papers even before publishing (its not peer reviews)(it is a preprint, not final paper))
2- IEEE Xplore
arXiv is a great starting point for free access to research papers. IEEE Xplore offer more comprehensive and high-quality content, especially in the field of autonomous vehicle computer vision.
This website shows (Active Venues and open for Submissions Every Month): Link
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Research Papers vs Journals vs Scholarly Articles?
Scholarly Articles:
Scholarly articles are short to medium-length pieces of academic writing.
They are often written by experts, researchers, or scholars in a specific field or discipline.
Scholarly articles are typically published in scholarly journals.
They focus on specific research topics, experiments, studies, or case analyses.
They follow a formal structure with sections like abstract, introduction, methodology, results, discussion, and references.
Scholarly articles are peer-reviewed, meaning they undergo a rigorous review process by experts in the field before publication.
They often contain original research findings, data, and conclusions.
Research Papers:
Research papers are comprehensive documents that provide in-depth coverage of a particular research topic.
They can vary in length from a few pages to hundreds of pages, depending on the complexity of the research.
Research papers can be published in various formats, including conference papers, working papers, and technical reports.
They present detailed research methodologies, findings, analysis, and conclusions.
Research papers can be peer-reviewed, but not all are; some are considered preprints or working papers and may not have undergone formal peer review.
They are used to disseminate research results to the academic community and often serve as references for future research.
Journals:
Journals are periodical publications that regularly release a collection of articles, papers, and reviews.
Academic journals cover a wide range of subjects and disciplines, from science and technology to humanities and social sciences.
They serve as platforms for researchers to publish their scholarly articles and research papers.
Journals can be open-access (freely accessible to all) or subscription-based (requiring a subscription or purchase).
Articles published in journals are typically peer-reviewed, ensuring quality and credibility.
Journals can be ranked by impact factor or other metrics, indicating their influence and importance in a particular field.
In summary, scholarly articles and research papers are specific types of content often published within academic journals. Scholarly articles are shorter, focus on a single study or topic, and are rigorously peer-reviewed. Research papers, on the other hand, can be more extensive and may cover broader research areas. Journals, as periodical publications, encompass a wide range of research articles, reviews, and other content, serving as important outlets for academic research dissemination.
How to write a IEEE paper: Link
A Checklist for Submitting Your Research to arXiv: Link
Curvenote: Link
HOW TO: Submit Paper From Overleaf to arXiv: Link
Example of Write & Overleaf latex paper to arxiv: Link
LAST WORDS:-
One thing to keep in the MIND Ai and self-driving Car technologies are very vast...! Don't compare yourself to others, You can keep learning..........
Competition And Innovation Are Always happening...!
so you should get really Comfortable with change...
So keep slowly Learning step by step and implement, be motivated and persistent
I hope you really Learn something from This Blog
Bye....!
BE MY FRIEND🥂
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