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The User Acceptance in Autonomous Vehicle is a main subject that has received the attention of researcher and professional in all around the world. The present research reviews literature which demonstrates the nature of technological acceptance is mediated by distinct factor groups related to the psychology of the users, the design process of information technology, and the quality of the technology in user terms.
All benefit, after technological difficulties, do not come without a certain amount of challenges. The present challenges of Autonomous Vehicle are Assurance of system and Software, Sensing and Connectivity, Judgment, and Verification and Validation.
In autonomous vehicle there are problem and issue that many of the people have not yet encountered or even witnessed. To better predict, explain and increase User acceptance, we need to better understand why people accept or reject Autonomous Vehicle. This research address the ability to predict peoples, User acceptance from a measure of their intention, and the ability to explain their intension in term of their attitude, perceived usefulness, perceived ease of use. A survey was conducted to know the intension of User towards this technology and in what extend people are aware and have knowledge about the Autonomous Vehicle. In a Study few questions were asked to know user view towards this technology and then, we try to understand what they think of Autonomous Vehicle and their willingness to User acceptance on Autonomous Vehicle. By doing so we can determine what exactly the user expects from this technology and where government and car manufacture are lacking behind to promote the Acceptance in Autonomous Vehicle. Implementation and measure are taken according to the evaluation of the survey.
User Acceptance, Autonomous Technology, User behavior, perceived usefulness, Ease of technology, Road safety
Autonomous vehicles are “such vehicles that are able to perceive their environment and to move on without any intervention of a human driver'. These vehicles are also known as driverless, self driving, unmanned or robotic vehicles. Autonomous Vehicles are currently being developed in a number of commercial and research projects worldwide. While millions of dollars are already invested on this technology Car manufacturers are always looking to stay one step ahead of automobile trends so they can develop products that consumers and commercial fleet purchasers will buy in the future. The main objective of this paper is to promote User Acceptance on Autonomous Vehicle. In doing so there are many questioning asked by every individual which has to be answered. Among those question there is a common questions asked by every user “HOW SAFE YOU ARE IN AUTONOMOUS VEHICLE”?
To answer this question it’s better to providing more information about Autonomous Driving to the users to give more clarity and distinguish between User Driving and Autonomous Driving.
And there are many other issues are available in Autonomous Vehicle such as Ethics, Acceptance, Rights & Liability, Security, and Environment etc... Among this the main issue is the user acceptance that needs to be dealt with in order for autonomous vehicles to be successfully introduced to the market. The survey is conducted to know the views on User acceptance on Autonomous Vehicle which would help the Government as well as the automotive industry who might benefit from the introduction of autonomous vehicles. This research looks into the factors that influence the user acceptance of self-driving cars through a series of interviews, after which the implications of these factors the government and the automotive industry are considered.
This section of the report aims to deepen the understanding of previously done research in the field of autonomous vehicles and the user acceptance of the technology, as well as to define the gaps in the literature. To do so, it first provides a background on both the autonomous vehicles and technology acceptance on a more general level, after which factors that are found to impact user acceptance of self-driving cars in existing literature are listed. These factors will help in answering the research question.
First of all, the methods of searching for existing literature are mentioned, after which the relevant topics and definitions are explained. Then, an overview of Autonomous driving cars and their levels of automation is given, as well as the benefits that Autonomous driving cars offer.
In this section, definitions are given for the term User Acceptance and factors that influence it, as well as the term Autonomous Vehicles. The way levels of automation are defined according to the Society of Automotive Engineers (SAE) is clarified as well.
User acceptance is a complex construct that consists of many factors that play a role in it. Dillon and Morris (1996) define user acceptance in the context of information technology as the demonstrable willingness within a user group to employ a technology for the tasks it is designed to support. Davis (1985) states that two other constructs, namely perceived usefulness and perceived ease of use, affect user acceptance of technologies. This research will try to identify similar constructs, which will be referred to as factors that influence the user acceptance of self-driving cars defined according to the definition of Dillon and Morris (1996).
It is also important to make a distinction between two types of acceptance, referred to as consumer acceptance and citizen acceptance by Huijts et al. (2012). The first refers to the willingness to use the technology itself, while the latter refers to the placement of the technology in one’s environment. Since these two forms of acceptance influence each other and cannot completely be seen as separate issues, both are seen as part of the term user acceptance used in this research. Since this paper will mostly address people with a driver’s license however, and since it is assumed that it will play a larger role in the success of the technology, a larger emphasis is placed on consumer acceptance.
Autonomous vehicles are “such vehicles that are able to perceive their environment and to move on without any intervention of a human driver'.
Wood, Chang, Healy, and Wood (2012) mention that although their article generally uses the term “autonomous” instead of “automated”, despite the fact that the latter term is perhaps more accurate. The reason for this is that the term “autonomous” is currently in more widespread use and therefore also more familiar to the general public. The authors argue that the term “automated” refers to control by a machine, while “autonomous” refers to acting alone or independently. In this article, the term “autonomous” will also be used despite its inaccuracy, for reasons of familiarity with the term. The SAE refers to “automated” vehicles and has identified several levels of automation, which will be discussed in the next subsection.
The Society of Automobile Engineers (SAE) defined five levels of autonomous driving. Each level is described by a set of minimal capabilities of the vehicle and a certain vehicle can operate at different levels, depending on their activated automation.
The most important distinction between the levels of automation is the step from SAE Level 2 to 3, which separates Human Driver Systems from Automated Driving Systems, as can be seen in Figure 2-1. This paper focuses only on the type of cars that are labeled by the SAE as Automated Driving Systems (Level 4&5), as user acceptance is expected to be a significantly larger barrier for these vehicles
Figure 2.2 Summary table of levels of driving automation
Autonomous vehicle is a combination of different sensor which is used to understand the world around them to get you where you needed to go.
There is sensor such as
GPS: Triangulates position of car using satellites. Current GPS technology is limited to a certain distance. Advanced GPS is in development.
LIDAR: Measures distance by illuminating target with pulsed laser light and measuring reflected pulses with sensors to create 3-D map of area.
Cameras: Provide real-time obstacle detection to facilitate lane departure and track roadway information (like road signs).
Ultrasonic Sensors: Uses high-frequency sound waves and bounce-back to calculate distance. Good in close range.
Central Computer: “Brain” of the vehicle. Receives information from various components and helps direct vehicle overall.
Radar: Radio waves detect short & long-range depth
DRSC - Based Receiver: Communications device permitting vehicle to communicate with other vehicles (V2V) using DSRC, a wireless communication standard that enables reliable data transmission in active safety applications.
Figure 3 How Autonomous Work
With these sensors it combines the information and it identify everything around it in fully 360 degree then predict the things might do next. With the knowledge of traffic it can plan and safe path ahead. It also helps to you and people around you to feel secure and ease with this technology.
According to World Health Organization (WHO), globally every year road accidents claim the lives of 1.3 million people in the worldwide. This continues to be a life-threatening issue to individuals and their families, not just in Europe, but across the world in both developing and developed Countries. The number of road deaths is on the rise again even in some countries with impressive road safety improvements. The increasing share of vulnerable road users such as seniors, pedestrian, cyclists and motorcyclists that become victims of road traffic raises particular concerns. Fatalities and injuries resulting from road traffic accidents are a major and growing public health problem in World. Traffic accidents in many developing countries have become one of the leading causes of deaths. It also listed the current national road safety strategies and legislation in place regarding speed limits, drinking and driving and the use of seat belt and helmet. Reliable data on traffic crashes is crucial for effective action on road safety. Without hard facts about the scale of the problem, the exposure to crash risks and the effectiveness of policies the problems cannot be addressed at the core. The aim is to provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety. Today, most countries have national road safety strategies in place, often with ambitious targets.
In all around the world the road accident factors are mainly related to driver faults. According WHO, majority of vulnerable road user are pedestrian, cyclist and motorcycle are victim of road accident.
In the below table it shown that, according to WHO and Pines Salomon Injury Lawyers (USA) a private firm in USA the reason of the major road accident occur due to. Pines Salomon Injury Lawyers (USA) is a private insurance firm according to them the factor for the road accident in US
Reliable data on traffic crashes is crucial for effective action on road safety. Without hard facts about the scale of the problem, the exposure to crash risks and the effectiveness of policies the problems cannot be addressed at the core.
In US 80% of accident occur due to distract driving and in whole world it’s due to speeding.
By given factor due to which road accident takes place maximum of factors can be eliminated by Autonomous Vehicle.
Table: List of accident in world and U.S.A
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In 2015, Germany recorded an increase in road deaths to 3 459, a 2.5% increase over 2014. This is equivalent to a mortality rate of 4.3 per 100 000 inhabitants. Pedestrians, car occupants and in particular motorcyclists were most affected by this increase. However, provisional data from 2016 shows a 7% decrease in fatalities to 3 214. Since 2000, fatalities on the roads have been reduced by 54%, although injury crashes have not benefitted from the same level of improvement.
Between 1991 and 2015, the number of fatalities decreased by 70%, whereas the number of injury crashes fell by only 20.6%. In recent years (2000-15), the number of fatalities decreased by 54%. While the number of seriously injured decreased only by 34%. The decreased in the number of crashes and fatalities is due to various changes in all fields of road safety traffic safety-related behavior and education as well as infrastructure and vehicle safety. The improvement in road safety are due to several measures taken and regulations introduced in the past 10 years like road safety education in school, accompanied driving proramme and alcohol prohibition for novice drivers, road safety audits and treatment of accident black spots.
Since 1991, the death rate per 100.000 inhabitants has decreased by 70%, while the number of vehicles registered per 1000 inhabitants has increased by 22%.
Figure 5.1 Road Accidents in Germany
Germany is one the world’s most highly motorized countries. Fatalities among motor vehicle occupants and pedestrians have gradually decreased since 1991, with the reduction being strongest for passenger car occupants (-76%).
In 2015, an increase in the number of fatalities was observed among pedestrians (+2.7%), car occupants (+2.9%) and motorcyclists (+8.9%).
The strong reduction in fatalities was observed for moped riders (-28.7%), with a more moderate improvement for cyclist (-3.3%)
Traffic crashes represent a significant cost for society, estimated in 2015 at around EUR 34.4 billion, or 1.1% of Germany’s GDP. It is estimated that since 2005 crash costs have increased by 9%. These figures do not include an estimation of costs of non-reported crashes.
Speed: Inappropriate speed was a factor in nearly 34% of fatal crashes and about 15% of injury crashes in 2015. Speed is often cited as a factor in combination with other high-risk behavior, such as drink driving.
Drink and Driving: Driving with blood alcohol content (BAC) over 0.5 g/l is punishable by a fine, license suspension and possibly jail. In addition, drivers with a BAC between 0.3 g/l and 0.5 g/l can have their license suspended if their driving ability is impaired. In 2015, alcohol use was cited as a contributing factor in 7.2% of all fatal crashes. The number of alcohol related crashes and fatalities have decreased continuously over recent years by 12% and 25% respectively since 2010.
Drug and Driving: In 2015, there were 1 679 drug-related crashes in Germany causing 43 fatalities and 2 304 injuries. The figures have risen from the 2000 level of 1 015, both from possible increased drug use as well as better education within the police agencies on detecting the influence of drugs.
Distraction: Estimation of fatalities due to the use of mobile phones, based on the official accident statistics, is not possible, since mobile phone use is not assessed in the course of the collection of crash data by the police agencies.
In this section of the report, the research design and methodology used will be elaborated upon. First, section 6.1 will discuss the way in which data is collected, after which section 6.2 will discuss how the collected data will be analyzed in order to come to results. Afterwards, the chapter implications will be discussed.
Data was collected over a time period of approximately one month during this research project. In survey were used to collect the data, but different target audiences were addressed. The data collected in survey was required for answering the User views towards Autonomous Vehicle.
In the first part of this research, 12 question interviews, lasting approximately 7-8 minutes, will be conducted. Since getting a representative sample of the entire population would require a much larger sample size, which is impractical mainly due to time constraints, this thesis research focuses specifically on a younger audience. This group of people is deemed to be more interesting, as they are more likely to have an interest in Autonomous cars and to have developed a mental model of the technology for themselves. Another reason why this target group is selected is the fact that younger people are more likely to come in contact with Autonomous cars in the future, as the technology might still take some time to mature.
A survey was conducted in which 13 candidates took part in it. The questions during this survey were open in nature, 12 questions were asked in the survey. Among that the question asked in end of this survey, the candidates were presented with a table of previously identified factors, and asked to rank the 5 most important factors influencing their personal acceptance of self-driving cars.
In order to answer the second research question, the findings and conclusions of the first research question were used in combination with questions asked during the survey about the role of the government and the manufacturers, leading to a number of implications for these parties.
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