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 Development and Verification of Infrastructure-Assisted Automated Driving Functions

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2161
Author(s):  
Martin Rudigier ◽  
Georg Nestlinger ◽  
Kailin Tong ◽  
Selim Solmaz
Keyword(s):  
Driving Safety ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Human Driver ◽  
Decision Making Processes ◽  
Level 3 ◽  
Sensor Information ◽  
Vehicle Platoons ◽  
Eu Project ◽  
The Eu

Automated vehicles we have on public roads today are capable of up to SAE Level-3 conditional autonomy according to the SAE J3016 Standard taxonomy, where the driver is the main responsible for the driving safety. All the decision-making processes of the system depend on computations performed on the ego vehicle and utilizing only on-board sensor information, mimicking the perception of a human driver. It can be conjectured that for higher levels of autonomy, on-board sensor information will not be sufficient alone. Infrastructure assistance will, therefore, be necessary to ensure the partial or full responsibility of the driving safety. With higher penetration rates of automated vehicles however, new problems will arise. It is expected that automated driving and particularly automated vehicle platoons will lead to more road damage in the form of rutting. Inspired by this, the EU project ESRIUM investigates infrastructure assisted routing recommendations utilizing C-ITS communications. In this respect, specially designed ADAS functions are being developed with capabilities to adapt their behavior according to specific routing recommendations. Automated vehicles equipped with such ADAS functions will be able to reduce road damage. The current paper presents the specific use cases, as well as the developed C-ITS assisted ADAS functions together with their verification results utilizing a simulation framework.

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.

scholarly journals Does Adaptive Mode Transition Contribute to Better Driver Intervention in Highly Automated Driving?

2019 ◽  
Vol 63 (1) ◽  
pp. 287-291
Author(s):  
Huiping Zhou ◽  
Makoto Itoh ◽  
Satoshi Kitazaki
Keyword(s):  
Driving Simulator ◽  
Mode Transition ◽  
Automated Driving ◽  
Automated Control ◽  
Human Driver ◽  
Level 3 ◽  
Level Transition ◽  
Verbal Messages ◽  
Level 2 ◽  
Highly Automated Driving

This paper presents an adaptive mode (level) transition in highly combined driving automation in which the mode of a system could adaptively shift to any level including SAE level 3 (conditional automation, CA) to level 2 (partial automation) based on the driving environment. We show the effects of the adaptive transition on the take over of car control by a human driver and driving behavior after intervention when the system issues a response to intervene. A driving simulator experiment is conducted to collect data during the transition from automated control to manual driving in three scenes: obstacle on a driving lane, blurred lane mark, and stopped car ahead. Results indicate that the interventions of drivers who experience the adaptive transition are delayed in comparison to those who experience only the fixed transition. The adaptive transition is conducive for drivers to stop the car for preventing a potential collision with a stopped car ahead. Owing to the adaptive transition, drivers perceive a critical hazard after taking over car control and provide a rapid response. In addition, during the adaptive transition, drivers prefer verbal messages to the simple “beeping” message.

scholarly journals A Study on Re-Engagement and Stabilization Time on Take-Over Transition in a Highly Automated Driving System

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 344
Author(s):  
Hyunsuk Kim ◽  
Woojin Kim ◽  
Jungsook Kim ◽  
Seung-Jun Lee ◽  
Daesub Yoon ◽  
...  
Keyword(s):  
Human Factors ◽  
Response Times ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Traffic Density ◽  
Automated Driving ◽  
Stabilization Time ◽  
Factors Affecting ◽  
Cart Analysis ◽  
Level 3

In the case of level 3 automated vehicles, in order to safely and quickly transfer control authority rights to manual driving, it is necessary that a study be conducted on the characteristics of human factors affecting the transition of manual driving. In this study, we conducted three experiments to compare the characteristics of human factors that influence the driver’s quality of response when re-engaging and stabilizing manual driving. The three experiments were conducted sequentially by dividing them into a normal driving situation, an obstacle occurrence situation in front, and an obstacle and congestion on surrounding roads. We performed a statistical analysis and classification and regression tree (CART) analysis using experimental data. We found that as the number of trials increased, there was a learning effect that shortened re-engagement times and increased the proportion of drivers with good response times. We found that the stabilization time increased as the experiment progressed, as obstacles appeared in front and traffic density increased in the surrounding lanes. The results of the analysis are useful for vehicle developers designing safer human–machine interfaces and for governments developing guidelines for automated driving systems.

scholarly journals Human–Vehicle Integration in the Code of Practice for Automated Driving

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 284 ◽  
Author(s):  
Stefan Wolter ◽  
Giancarlo Caccia Dominioni ◽  
Sebastian Hergeth ◽  
Fabio Tango ◽  
Stuart Whitehouse ◽  
...  
Keyword(s):  
Human Factors ◽  
Development Process ◽  
The Body ◽  
Automated Driving ◽  
The Public ◽  
Code Of Practice ◽  
The Past ◽  
Literature Reviews ◽  
Level 3 ◽  
Level 1

The advancement of SAE Level 3 automated driving systems requires best practices to guide the development process. In the past, the Code of Practice for the Design and Evaluation of ADAS served this role for SAE Level 1 and 2 systems. The challenges of Level 3 automation make it necessary to create a new Code of Practice for automated driving (CoP-AD) as part of the public-funded European project L3Pilot. It provides the developer with a comprehensive guideline on how to design and test automated driving functions, with a focus on highway driving and parking. A variety of areas such as Functional Safety, Cybersecurity, Ethics, and finally the Human–Vehicle Integration are part of it. This paper focuses on the latter, the Human Factors aspects addressed in the CoP-AD. The process of gathering the topics for this category is outlined in the body of the paper. Thorough literature reviews and workshops were part of it. A summary is given on the draft content of the CoP-AD Human–Vehicle Integration topics. This includes general Human Factors related guidelines as well as Mode Awareness, Trust, and Misuse. Driver Monitoring is highlighted as well, together with the topic of Controllability and the execution of Customer Clinics. Furthermore, the Training and Variability of Users is included. Finally, the application of the CoP-AD in the development process for Human-Vehicle Integration is illustrated.

scholarly journals Autonomous Driving – How to Apply Safety Principles

Dependability ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 21-33
Author(s):  
H. Schӓbe
Keyword(s):  
Autonomous Driving ◽  
Automated Driving ◽  
Safety Standards ◽  
The Road ◽  
Safe Life ◽  
Level Of Automation ◽  
Human Driver ◽  
On The Road ◽  
Fail Safe ◽  
Automatic Systems

We discuss safety principles of autonomous driving road vehicles. First, we provide a comparison between principles and experience of autonomous or automatic systems on rails and on the road. An automatic metro operates in a controlled and well-defined environment, passengers and third persons are separated from driving trains by fences, tunnels, etc. A road vehicle operates in a much more complex environment. Further, we discuss safety principles. The application of safety principles (e.g. fail-safe or safe-life) is used to design and implement a safe system that eventually fulfils the requirements of the functional safety standards. The different responsibility of human driver and technical driving system in different automation levels for autonomous driving vehicles require the application of safety principles. We consider, which safety principles have to be applied using general safety principles and analysing the relevant SAE level based on the experience from projects for the five levels of automated driving as defined by the SAE. Depending on the level of automation, the technical systems are implemented as fail-silent, fails-safe or as safe-life.

Assessing and Improving User Satisfaction in Higher Education: A Role for Human Factors

2004 ◽  
Author(s):  
William S. Helton ◽  
Jessica M. Neu ◽  
Tangy A. Shell ◽  
Alison J. Ramsey ◽  
Danielle M. Myers
Keyword(s):  
Higher Education ◽  
Human Factors ◽  
User Satisfaction

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

Autonomy and Responsibility in Hybrid Systems

2017 ◽  
Author(s):  
Wulf Loh ◽  
Janina Loh
Keyword(s):  
Hybrid Systems ◽  
Hybrid System ◽  
Moral Dilemma ◽  
Autonomous Driving ◽  
Shared Responsibility ◽  
Driving System ◽  
Autonomous Cars ◽  
Self Driving Cars ◽  
Trolley Cases ◽  
Autonomous Driving System

In this chapter, we give a brief overview of the traditional notion of responsibility and introduce a concept of distributed responsibility within a responsibility network of engineers, driver, and autonomous driving system. In order to evaluate this concept, we explore the notion of man–machine hybrid systems with regard to self-driving cars and conclude that the unit comprising the car and the operator/driver consists of such a hybrid system that can assume a shared responsibility different from the responsibility of other actors in the responsibility network. Discussing certain moral dilemma situations that are structured much like trolley cases, we deduce that as long as there is something like a driver in autonomous cars as part of the hybrid system, she will have to bear the responsibility for making the morally relevant decisions that are not covered by traffic rules.

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