scholarly journals Autonomous Recognition Model

2021 ◽  
Vol 10 (6) ◽  
pp. 67-73
Author(s):  
Akash gupta ◽  
Rahat Ali ◽  
Abhay Pratap Singh ◽  
P.Raja Kumar
Keyword(s):  
Artificial Intelligence ◽  
Automobile Industry ◽  
Autonomous Vehicles ◽  
Future Generation ◽  
Autonomous Driving ◽  
Traffic Signs ◽  
Use Of Technology ◽  
Human Driver ◽  
New Research ◽  
Self Driving Cars

Nowdays we are witnessing the technology transforming everything the way we used to do things and how the automobile industry is transforming itself with the use of technology IOT,Artificial intelligence,Machine learning.Companies shifting its products and its utilities in diferent way and they now want to acquire and introduce level-5 autonomous to future generation and big automobile companies are trying to achieve autonomous vechicles and we have researhed about the model that will help in assisting autonomous vechicles and trying to achieve that.We will develop this model with help of technologies like Artificial intelligence,Machine learning,Deep learning.Autonomous vehcicles will become a reality on our roads in the near future. However, the absence of a human driver requires technical solutions for a range of issues, and these are still being developed and optimised. It is a great contribution for the automotive industry which is going towards innovation and economic growth. If we talking about some past decade the momentum of new research and the world is now at the very advanced stage of technological revolution. “Autonomous-driving” vehicles. The term Self-driving cars, autonomous car, or the driverless cars have different name with common objective. The main focus is to keep the human being out of the vehicle control loop and to relieve them from the task of driving. Everyday automotive technology researchers solve challenges. In the future, without human assistance, robots will produce autonomous vehicles using IoT technology based on customer needs and prefer that these vehicles are more secure and comfortable in mobility systems such as the movement of people or goods. We will build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles .This model we have tested it and resulted in 95% accuracy.

The ideological divide in public perceptions of self-driving cars

2020 ◽  
Vol 29 (4) ◽  
pp. 436-451
Author(s):  
Yilang Peng
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Scientific Literacy ◽  
Autonomous Vehicles ◽  
The United States ◽  
Public Perceptions ◽  
New Information ◽  
Nationally Representative ◽  
Self Driving Cars ◽  
Social Conservatism

Applications in artificial intelligence such as self-driving cars may profoundly transform our society, yet emerging technologies are frequently faced with suspicion or even hostility. Meanwhile, public opinions about scientific issues are increasingly polarized along the ideological line. By analyzing a nationally representative panel in the United States, we reveal an emerging ideological divide in public reactions to self-driving cars. Compared with liberals and Democrats, conservatives and Republicans express more concern about autonomous vehicles and more support for restrictively regulating autonomous vehicles. This ideological gap is largely driven by social conservatism. Moreover, both familiarity with driverless vehicles and scientific literacy reduce respondents’ concerns over driverless vehicles and support for regulation policies. Still, the effects of familiarity and scientific literacy are weaker among social conservatives, indicating that people may assimilate new information in a biased manner that promotes their worldviews.

scholarly journals Research and Educational Programs for Connected and Autonomous Vehicles at the University of Waterloo

2017 ◽  
Vol 139 (12) ◽  
pp. S21-S23
Author(s):  
Ross Mckenzie ◽  
John Mcphee
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Engineering Students ◽  
Pattern Analysis ◽  
Autonomous Driving ◽  
Educational Programs ◽  
Transportation Systems ◽  
Systems Research ◽  
Vehicle Systems ◽  
The University

This article presents an overview of the research and educational programs for connected and autonomous vehicles at the University of Waterloo (UWaterloo). UWaterloo is Canada’s largest engineering school, with 9,500 engineering students and 309 engineering faculty. The University of Waterloo Centre for Automotive Research (WatCAR) for faculty, staff and students is contributing to the development of in-vehicle systems education programs for connected and autonomous vehicles (CAVs) at Waterloo. Over 130 Waterloo faculty, 110 from engineering, are engaged in WatCAR’s automotive and transportation systems research programs. The school’s CAV efforts leverage WatCAR research expertise from five areas: (1) Connected and Autonomous; (2) Software and Data; (3) Lightweighting and Fabrication; (4) Structure and Safety; and (5) Advanced Powertrain and Emissions. Foundational and operational artificial intelligence expertise from the University of Waterloo Artificial Intelligence Institute complements the autonomous driving efforts, in disciplines that include neural networks, pattern analysis and machine learning.

scholarly journals A Few Critical Human Factors for Developing Sustainable Autonomous Driving Technology

2020 ◽  
Vol 12 (7) ◽  
pp. 3030
Author(s):  
José Fernando Sabando Cárdenas ◽  
Jong Gyu Shin ◽  
Sang Ho Kim
Keyword(s):  
Human Factors ◽  
User Satisfaction ◽  
Autonomous Driving ◽  
Software Components ◽  
Automated Driving ◽  
Human Errors ◽  
Learning Speed ◽  
Human Driver ◽  
Level 3 ◽  
Self Driving Cars

The purpose of this study is to develop a framework that can identify critical human factors (HFs) that can generate human errors and, consequently, accidents in autonomous driving level 3 situations. Although much emphasis has been placed on developing hardware and software components for self-driving cars, interactions between a human driver and an autonomous car have not been examined. Because user acceptance and trust are substantial for the further and sustainable development of autonomous driving technology, considering factors that will influence user satisfaction is crucial. As autonomous driving is a new field of research, the literature review in other established fields was performed to draw out these probable HFs. Herein, interrelationship matrices were deployed to identify critical HFs and analyze the associations between these HFs and their impact on performance. Age, focus, multitasking capabilities, intelligence, and learning speed are selected as the most critical HFs in autonomous driving technology. Considering these factors in designing interactions between drivers and automated driving systems will enhance users’ acceptance of the technology and its sustainability by securing good usability and user experiences.

scholarly journals Switching from autopilot to the driver: A transient performance analysis

2019 ◽  
Vol 234 (5) ◽  
pp. 1346-1360 ◽  
Author(s):  
Seshan Ramanathan Venkita ◽  
Dehlia Willemsen ◽  
Mohsen Alirezaei ◽  
Henk Nijmeijer
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous System ◽  
A Priori ◽  
Autonomous Driving ◽  
Special Focus ◽  
Modal Control ◽  
Driving System ◽  
Lateral Dynamics ◽  
Human Driver ◽  
Autonomous Driving System

One of the main safety concerns associated with semi-autonomous vehicles is the sharing of control between a human driver and an autonomous driving system. Even with an attentive driver, such switches in control may pose a threat to the safety of the driver and the surrounding vehicles. The aim of this study is to develop an indicator that can measure the level of safety during a driver take-over, using knowledge about the system known a priori. A model-based approach is used to analyse the system with special focus on the lateral dynamics of the vehicle. The driver and the vehicle are modelled as linear systems, and a path tracking controller is used to serve as an autonomous system. With this structure, shared control is studied as a switched system, in which the vehicle’s lateral control switches between the autonomous system and the driver. A bound on the transient dynamics that arise due to a switch is derived, using the induced [Formula: see text] norm. This bound is then used to formulate an indicator that checks if the states/outputs of interest are within acceptable limits. A comparison with simulation results has shown that the indicator successfully captures the effect of different system parameters on take-over safety, although in a slightly conservative manner. This indicator can be further developed as a tool to be used in the design and evaluation of shared-/multi-modal control systems in future vehicles.

scholarly journals The Acceptance of Independent Autonomous Vehicles and Cooperative Vehicle-Highway Autonomous Vehicles

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 346
Author(s):  
Shuang Zhang ◽  
Peng Jing ◽  
Gang Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Equation Model ◽  
Autonomous Vehicles ◽  
Structural Equation ◽  
Autonomous Driving ◽  
Equation Model ◽  
Use Of Technology ◽  
Artificial Neural ◽  
Input Variables

The public’s acceptance of independent autonomous vehicles and cooperative vehicle-highway autonomous vehicles is studied by combining the structural equation model and an artificial neural network. The structural equation model’s output variables are used as the input variables of the artificial neural network, which compensates for the linear problem of the structural equation model and ensures the accuracy of the input variables of the artificial neural network. In order to summarize the influencing factors of autonomous vehicles acceptance, the Unified Theory of Acceptance and Use of Technology model was expanded by adding two variables: risk expectation and consumer innovation. The results show that social influence is the strongest predictor of the acceptance of independent autonomous vehicles. The most significant factor of the cooperative vehicle-highway autonomous vehicles’ acceptance is effort expectation. Additionally, risks, performance, existing traffic conditions, and personal innovation awareness also significantly affect autonomous driving acceptance. The research results can provide a theoretical basis for technology developers and industry managers to develop autonomous driving technology and policymaking.

scholarly journals Human factors concern on autonomous vehicles’ safety, ethics and cost saving for the ridesharing industries

2021 ◽  
Vol 11 (9) ◽  
pp. 2331-2340 ◽  
Author(s):  
Bankole K. Fasanya ◽  
Abosede O. Gbenga-Akinbiola
Keyword(s):  
Artificial Intelligence ◽  
Cost Saving ◽  
Business Model ◽  
Autonomous Vehicles ◽  
Public Perception ◽  
Autonomous Driving ◽  
Transportation Industry ◽  
Comprehensive Understanding ◽  
Technological Readiness ◽  
Autonomous Technology

Artificial Intelligence (AI) is a motivation for full usage of autonomous driving. Many have predicted that autonomous technology would significantly disrupt the transportation industry. This research examines how autonomous driving might impact and disrupt the ridesharing industry and their drivers. The hypothesis is that autonomous vehicles (AV) will negatively impact the ridesharing industry. To examine the full effects of this disruption, we researched current literature on driverless technology cars and the ridesharing industry. Factors examined include: current economics of drivers and vehicles, public perception and acceptance, technological readiness, collaborations, regulations, and liability. Key findings from a host of resources were tabulated to build a case for the proposed hypothesis. The results provide a more comprehensive timeline estimate, predicted $0.75 cost estimate per mile by 2040, and documented the collaboration figure among the players that shows the significant investments across different industries. This research shows that the ridesharing industry’s current business model is due for a significant disruption by autonomous driving capabilities. Drivers in the ridesharing industry might likely suffer the most, however not for at least another decade or so. There are many independent factors, which must be further scrutinized to develop a more comprehensive understanding as to the velocity of this disruption. Findings from this study would be applicable while evaluating the future of autonomous vehicles.

scholarly journals Autonomous Drifting Using Reinforcement Learning

2021 ◽  
Author(s):  
László Orgován ◽  
Tamás Bécsi ◽  
Szilárd Aradi
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
The Road ◽  
Model Free ◽  
On The Road ◽  
Self Driving Cars ◽  
Planning Problems

Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. The algorithm is based on a model-free learning algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3). The model is trained on six different tracks in a simulator, which is developed specifically for autonomous driving systems; namely CARLA.

scholarly journals Application of Artificial Intelligence in Tesla- A Case Study

2021 ◽  
pp. 205-218
Author(s):  
Divya Kumari ◽  
Subrahmanya Bhat
Keyword(s):  
Artificial Intelligence ◽  
Automobile Industry ◽  
Financial Services ◽  
Autonomous Vehicles ◽  
Oil And Gas ◽  
Route Planning ◽  
Case Study Report ◽  
Deep Layers ◽  
Car Park

Background/Purpose: Artificial intelligence algorithms are like humans, performing a task repeatedly, each time changing it slightly to maximize the result. A neural network is made up of several deep layers that allow for learning. Financial services, ICT, life science, oil and gas, retail, automotive, industrial healthcare, and chemicals and manufacturing sectors are among the industries that employ these algorithms. The electric motor is a new concept, and the automobile industry is now undergoing intensive research to determine whether it is practicable and financially viable. There are already some first movers, such as Tesla, who have successfully established their model and are moving forward. Tesla is forcing the auto industry to adapt quickly. Tesla introduced Autopilot driver capability for its Model S vehicle. Tesla Autopilot is a suite of sophisticated driver-assist technologies that include traffic adjustment, congested roads navigation system, autopilot car-parks, computer-controlled road rules, semi-autonomous route planning on major roadways, and the ability to summon the vehicle out of a designated car-park. This article provides a comprehensive analysis of Tesla Company and Innovations of Autopilot Vehicles. Objective: This case study report addresses the growth of Tesla Company in the field of Autonomous Vehicles. Design/Methodology/Approach: The knowledge for this case study of Tesla was gathered from various academic articles, online articles, and the SWOT framework. Findings/Result: Based on the research, this paper discusses the technological histories, Autopilot driving features, safety concerns, financial plans, market challenges, different models, and how Tesla Inc. is accelerating the world's movement in multiple initiatives such as the contribution of the global economic system, study in the Artificial Intelligence and Machine Learning area. Originality/Value: This paper study provides a brief overview of Tesla Inc. given the various data collected, and information about Tesla Autopilot vehicles using Artificial Intelligence based Innovations in Entrepreneurial Oriented Cars. Paper type: A Research Case study paper - focuses on Application of Artificial Intelligence in Tesla Autopilot Vehicles and growth & Journey of the Tesla Inc. Company.

scholarly journals Multi-Cloud Resource Management Techniques for Cyber-Physical Systems

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8364
Author(s):  
Vlad Bucur ◽  
Liviu-Cristian Miclea
Keyword(s):  
Resource Management ◽  
Autonomous Vehicles ◽  
Cyber Physical Systems ◽  
Software Projects ◽  
Physical Systems ◽  
Cloud Resource Management ◽  
Human Driver ◽  
Resource Requirements ◽  
Self Driving Cars ◽  
Multi Cloud

Information technology is based on data management between various sources. Software projects, as varied as simple applications or as complex as self-driving cars, are heavily reliant on the amounts, and types, of data ingested by one or more interconnected systems. Data is not only consumed but is transformed or mutated which requires copious amounts of computing resources. One of the most exciting areas of cyber-physical systems, autonomous vehicles, makes heavy use of deep learning and AI to mimic the highly complex actions of a human driver. Attempting to map human behavior (a large and abstract concept) requires large amounts of data, used by AIs to increase their knowledge and better attempt to solve complex problems. This paper outlines a full-fledged solution for managing resources in a multi-cloud environment. The purpose of this API is to accommodate ever-increasing resource requirements by leveraging the multi-cloud and using commercially available tools to scale resources and make systems more resilient while remaining as cloud agnostic as possible. To that effect, the work herein will consist of an architectural breakdown of the resource management API, a low-level description of the implementation and an experiment aimed at proving the feasibility, and applicability of the systems described.

scholarly journals Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5450
Author(s):  
Sorin Grigorescu ◽  
Tiberiu Cocias ◽  
Bogdan Trasnea ◽  
Andrea Margheri ◽  
Federico Lombardi ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Novel Technologies ◽  
Learning Modules ◽  
Network Bandwidth ◽  
Inference Engines ◽  
Control Units ◽  
Probable Path ◽  
Self Driving Cars ◽  
Most Probable Path

Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction.

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