scholarly journals Autonomous Driving using CNN

2021 ◽  
Vol 9 (VI) ◽  
pp. 3633-3636
Author(s):  
Amar V S
Keyword(s):  
Autonomous Driving ◽  
Learning Technologies ◽  
Human Beings ◽  
Automated Driving ◽  
Planning And Control ◽  
Long Run ◽  
End To End ◽  
Improved Performance ◽  
Will Self ◽  
Training Signal

Human beings are currently addicted to automation and robotics technologies. The state-of-the-art in deep learning technologies and AI is the subject of this autonomous driving. Driving with automated driving systems promises to be safe, enjoyable, and efficient.. It is preferable to train in a virtual environment first and then move to a real-world one. Its goal is to enable a vehicle to recognise its surroundings and navigate without the need for human intervention. The raw pixels from a single front-facing camera were directly transferred to driving commands using a convolution neural network (CNN). This end-to-end strategy proved to be remarkably effective, The system automatically learns internal representations of the essential processing stages such as detecting useful road components using only the human steering angle as the training signal. We never expressly taught it to recognise the contour of roadways, for example. In comparison to explicit issue decomposition, such as lane marking detection, Our end-to-end solution optimises all processing processes at the same time, including path planning and control. We believe that this will lead to improved performance and smaller systems in the long run. Internal components will self-optimize to maximise overall system performance, resulting in improved performance.

Independent wheel control system design for highly automated driving

2021 ◽  
pp. 095440702110065
Author(s):  
Shihuan Li ◽  
Lei Wang
Keyword(s):  
Control System ◽  
Control Method ◽  
Autonomous Driving ◽  
Control System Design ◽  
Steering Control ◽  
Automated Driving ◽  
Accuracy And Precision ◽  
Actual Observation ◽  
Control Scheme ◽  
Interference Measurement

For L4 and above autonomous driving levels, the automatic control system has been redundantly designed, and a new steering control method based on brake has been proposed; a new dual-track model has been established through multiple driving tests. The axle part of the model was improved, the accuracy of the transfer function of the model was verified again through acceleration-slide tests; a controller based on interference measurement was designed on the basis of the model, and the relationships between the controller parameters was discussed. Through the linearization of the controller, the robustness of uncertain automobile parameters is discussed; the control scheme is tested and verified through group driving test, and the results prove that the accuracy and precision of the controller meet the requirements, the robustness stability is good. Moreover, the predicted value of the model fits well with the actual observation value, the proposal of this method provides a new idea for avoiding car out of control.

scholarly journals End-to-End Autonomous Driving Through Dueling Double Deep Q-Network

2021 ◽  
Author(s):  
Baiyu Peng ◽  
Qi Sun ◽  
Shengbo Eben Li ◽  
Dongsuk Kum ◽  
Yuming Yin ◽  
...  
Keyword(s):  
Neural Network ◽  
State Space ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Autonomous Driving ◽  
Saliency Map ◽  
Hierarchical Architecture ◽  
Link Type ◽  
The Neural Network ◽  
End To End

AbstractRecent years have seen the rapid development of autonomous driving systems, which are typically designed in a hierarchical architecture or an end-to-end architecture. The hierarchical architecture is always complicated and hard to design, while the end-to-end architecture is more promising due to its simple structure. This paper puts forward an end-to-end autonomous driving method through a deep reinforcement learning algorithm Dueling Double Deep Q-Network, making it possible for the vehicle to learn end-to-end driving by itself. This paper firstly proposes an architecture for the end-to-end lane-keeping task. Unlike the traditional image-only state space, the presented state space is composed of both camera images and vehicle motion information. Then corresponding dueling neural network structure is introduced, which reduces the variance and improves sampling efficiency. Thirdly, the proposed method is applied to The Open Racing Car Simulator (TORCS) to demonstrate its great performance, where it surpasses human drivers. Finally, the saliency map of the neural network is visualized, which indicates the trained network drives by observing the lane lines. A video for the presented work is available online, https://youtu.be/76ciJmIHMD8 or https://v.youku.com/v_show/id_XNDM4ODc0MTM4NA==.html.

scholarly journals Using vision-based object detection for link quality prediction in 5.6-GHz channel

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Riichi Kudo ◽  
Kahoko Takahashi ◽  
Takeru Inoue ◽  
Kohei Mizuno
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Wireless Lan ◽  
Autonomous Robots ◽  
Learning Technologies ◽  
Link Quality ◽  
Automated Driving ◽  
Position Information ◽  
Prediction Scheme ◽  
Accurate Perception

Abstract Various smart connected devices are emerging like automated driving cars, autonomous robots, and remote-controlled construction vehicles. These devices have vision systems to conduct their operations without collision. Machine vision technology is becoming more accessible to perceive self-position and/or the surrounding environment thanks to the great advances in deep learning technologies. The accurate perception information of these smart connected devices makes it possible to predict wireless link quality (LQ). This paper proposes an LQ prediction scheme that applies machine learning to HD camera output to forecast the influence of surrounding mobile objects on LQ. The proposed scheme utilizes object detection based on deep learning and learns the relationship between the detected object position information and the LQ. Outdoor experiments show that LQ prediction proposal can well predict the throughput for around 1 s into the future in a 5.6-GHz wireless LAN channel.

scholarly journals Video Cloud Services for Hospitals: Designing an End-to-End Cloud Service Platform for Medical Video Storage and Secure Access

10.2196/18139 ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. e18139
Author(s):  
Piotr Pawałowski ◽  
Cezary Mazurek ◽  
Mikołaj Leszczuk ◽  
Jean-Marie Moureaux ◽  
Amine Chaabouni
Keyword(s):  
Video Quality ◽  
Cloud Service ◽  
Video Data ◽  
Cloud Services ◽  
Information And Communications Technology ◽  
Video Encoding ◽  
Rapid Expansion ◽  
Long Run ◽  
Generic Architecture ◽  
End To End

The amount of medical video data that has to be securely stored has been growing exponentially. This rapid expansion is mainly caused by the introduction of higher video resolution such as 4K and 8K to medical devices and the growing usage of telemedicine services, along with a general trend toward increasing transparency with respect to medical treatment, resulting in more and more medical procedures being recorded. Such video data, as medical data, must be maintained for many years, resulting in datasets at the exabytes scale that each hospital must be able to store in the future. Currently, hospitals do not have the required information and communications technology infrastructure to handle such large amounts of data in the long run. In this paper, we discuss the challenges and possible solutions to this problem. We propose a generic architecture for a holistic, end-to-end recording and storage platform for hospitals, define crucial components, and identify existing and future solutions to address all parts of the system. This paper focuses mostly on the recording part of the system by introducing the major challenges in the area of bioinformatics, with particular focus on three major areas: video encoding, video quality, and video metadata.

Indigenous Self-Governance and Development on American Indian Reservations

2021 ◽  
Vol 111 ◽  
pp. 233-237
Author(s):  
Dustin Frye ◽  
Dominic P. Parker
Keyword(s):  
American Indian ◽  
Indigenous People ◽  
Income Growth ◽  
Indian Reservations ◽  
Indian Reorganization Act ◽  
Trade Offs ◽  
Long Run ◽  
Per Capita ◽  
Will Self ◽  
American Indian Reservations

The UN Declaration on the Rights of Indigenous People promotes self-governance as a matter of justice rather than economics. How will self-governance affect the incomes of indigenous people? To gain insight, we compare long-run income growth on American Indian reservations with and without federal oversight through the 1934 Indian Reorganization Act. Reservations with more autonomy had 12-15 percent higher income per capita in 2016, even conditional on 1930s income. However, these more autonomous reservations also experienced wider income variance with more downside risk. The findings are consistent with theory emphasizing the development trade-offs between local and centralized governance.

Conclusion

2021 ◽  
pp. 161-164
Author(s):  
Eric A. Posner
Keyword(s):  
Artificial Intelligence ◽  
Labor Market ◽  
Labor Markets ◽  
Human Beings ◽  
Independent Contractors ◽  
Gig Economy ◽  
Long Run ◽  
The Social ◽  
Market Fragmentation ◽  
Social Surplus

Many people are worried about the fragmentation of labor markets, as firms replace employees with independent contractors. Another common worry is that low-skill work, and ultimately nearly all forms of work, will be replaced by robots as artificial intelligence advances. Labor market fragmentation is not a new phenomenon and can be addressed with stronger classification laws supplemented by antitrust enforcement. In fact, the gig economy has many attractive elements, and there is no reason to fear it as long as existing laws are enforced. Over the long run, artificial intelligence may replace much of the work currently performed by human beings. If it does, the appropriate response is not antitrust or employment regulation but policy that ensures the social surplus is fairly divided.

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