Are We Ready to Ride Autonomous Vehicles? A Pilot Study on Austrian Consumers’ Perspective
Abstract
:1. Introduction
Research Objective
2. Literature Review
2.1. Evolution of AVs
2.2. The Core of AV Technology
- What is the current status of AVs?
- Are AVs able to confidently identify different objects in their surroundings?
- When is it suitable, safe, and legal to move further?
2.3. Connected Vehicles (Car2X-Communication)
2.4. Levels of Automation—Process of Autonomous Driving
2.5. Timeline for Complete Autonomy
2.6. Few of the Most Discussed Benefits and Challenges of AVs
2.7. Point of View of Economic and Technical Trends—Car Sharing
3. Methodology
3.1. Data Collection
3.2. Analysis Technique
4. Results
4.1. Descriptive Statistics about Consumers’ Concerns towards AV Technology
4.2. Index Concerns
4.3. The Influence of Gender on People’s Concerns
4.4. Descriptive Statistics about the Feeling of Safety
4.5. Feeling of Safety
4.6. Influence of Place of Residence on People’s Concerns
4.7. Timeline
4.8. Attitudes of Consumers towards Car Sharing
4.9. Use Case Scenarios of an AV
4.10. The Motivation of Consumers to Lend/Share Their AV
4.11. Hypotheses Assessment Summary
5. Conclusions and Limitations
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Components | References | |
---|---|---|
Hardware | In terms of the hardware, self-driving cars use multiple sensors which aim to gather information about the vehicle itself and the nearby environment and send it to the car’s on-board computer. The speed, movement, and position of such items can be determined by sending out radio (continuous) waves which are naturally reflected off adjacent solid surfaces. It constitutes an indication that an object is there if a radio wave is sent out and reflected. Moreover, AVs are typically built on various other types of sensors, including inertial navigation systems, light detection and ranging (LIDAR), sound navigation ranging (SONAR), and video cameras. The multi-sensor data fusion enables safe autonomous driving. | [6,8,10] |
Navigation | AVs also count on digital maps for autonomous driving. Such maps contain geographical information as associated coordinates for any point on the road and overhead layouts of roads. Furthermore, the maps are often manually provided with additional information such as a traffic signals, signs, and traffic lights. Due to double-checking the preloaded-information with live sensor information (e.g., the usage of a video camera can ensure if there is a traffic signal for sure), the car can make more accurate driving decisions. | [10,11] |
Software | Software (coordinating computer system/artificial intelligence) is another crucial component of AV technology. The system is capable of organizing and planning all of the car’s activities. The system connects data from sensors and the map and uses sophisticated computer algorithms. For instance, Google’s self-driving car use the software called “Google-Chauffer.” The objective of the software is to take the passenger to its target location safely. In doing so, the software observes the rules of the road and additionally can recognize cars, objects, signs, and many random dangers such as cyclists and pedestrians, by interpreting all the communication between hardware and the ambiance. | [6,8,10,11,12] |
Level | Description | References |
---|---|---|
0 and 1 | At level 0 the driver is in full control all the time. Whereas, at level 1, some functional automation like anti-lock or skid control braking systems are in place. However, the driver always remains in command, with his/her foot on the pedal and hands on the wheel. | [21,23] |
2 | At this level, the driver is, in general, liable for monitoring the roadway and should always be available for control. This level is referred to as “combined function automation” by the National Highway Traffic Safety Administration (NHTSA). In general, a car at this level will change speed and keep itself in-lane on highways with the help of lane-sensing cameras, radar, and cruise control. | [21,23] |
3 | This level is referred to as “limited self-driving automation.” On this level, the drivers can give command of all safety-critical functions to the car’s on-board computer. However, an active attendance to take control is required. At this level, cars can take advantage of advance/adaptive cruise controls, surround cameras, sensors, radars, and other such technological gadgets to drive themselves to a certain distance. However, the active presence of the driver is always required. This technology is the least required to make possible the V2V communication, and it can drastically enhance an active platooning league for different cars. | [21,23,24,25,26,27,28] |
4 | Level four, which is known as “full self-driving automation” allows the driver to completely rely on the vehicle’s on-board computer and all its hardware and software technology in order to drive from one place to other. At this level, no active response from the driver/rider is needed. The car makes all critical decisions on its own. One piece of technology which plays the most important part in making a car self-driving is a combination of LIDAR and radio detection and ranging (RADAR). Both of which are used to sense different hurdles and objects en-route to the destination. However, significant technical improvements are necessary, and more years of development and testing are required before potential users (consumers) and regulators gain confidence in AVs. | [21,23,24] |
Benefits | Description | References |
---|---|---|
Congestion reduction | The concept of connected vehicles would cause some positive effects on congestion. Advantages of this technology are shorter gaps between vehicles, a higher and constant speed at traveling, which leads to a reduction in traffic-destabilizing shockwave propagation, and fuel savings. However, some other researches claim that due to the reliefs mentioned above, the overall vehicle miles traveled (VMT) will rise. | [7,8] |
Impacts on travel behavior | For older people, AVs would be a significant relief. Since fatal crash involvement (by VMT) climbs by age beginning in the mid-60s, many humans begin to restrict or stop driving. Yet not using cars can lead to considerable costs. For instance, if older adults do not use cars anymore, their personal freedom could be restricted. Innovations in personal mobility that ease the burden of age will be more critical over the coming decades. Additionally, with AVs, it would be possible to optimize fuel savings with a smart-parking decision. This system avoids “cruising for parking.” It is an in-vehicle system which shares data with parking infrastructure to get information for driverless drop-offs and pickups. | [37,38,39] |
Land use and ownership of vehicles | Owners of AVs could have a longer distance to travel to and from work because they can use their time for alternative activities. The better use of travel time leads to an increase in the total number of commuters. So, the introduction of self-driving cars could lead to more dispersed and low-density models of land use surrounding metropolitan regions. A consideration which may reduce the rate of car ownership is the program of AV sharing (more information can be found in Section 2.7). This concept would reduce the number of parking areas necessary and would allow for more significant development of cities. Over time, the whole concept of vehicle ownership would be shifted. In the long term, the number of vehicles in the national fleet should be reduced. | [7,8] |
Safety | Considering the benefits mentioned above of AVs, safety is an advantage which is directly concerned with consumers. That is why we have selected this benefit/advantage to be tested. Below we explain why AVs would prevent most automobile accidents. | [1,8] |
Challenges | Description | References |
---|---|---|
Trusting the technology | Fully autonomous vehicles have the potential to improve mobility. However, the trust to adopt these technologies is still missing from many consumers and may need to build-up over time. There may be some hesitation with full automation among older people who would benefit the most. Moreover, there are age differences in preferred methods for learning to use in-vehicle technologies. Younger participants were more likely to prefer the trial and error method or have a family or friend member explain the technology. Whereas younger and middle-aged adults favored the option of having the vehicle teach them how the technology is used, older adults were less interested in this idea. Using the manual or having the dealership explain the system is preferred by elderly people. | [5] |
Legal and insurance cost issues | As mentioned above, the trust of technology is an essential issue with the public. However, many other challenges still need to be addressed, including legal issues and the cost of technology. As explained above, with the introduction of AVs, connectivity of vehicles will rise. Therefore, massive investments in infrastructure will have to be provided. The cost of the technology could also be a barrier because network benefits can only be achieved when enough vehicles are on the road. With the introduction of AVs, legal issues will arise. Questions like “Are AVs legal?” and “Who is liable for damages in case of accidents?” come up. At the moment, the future for laws and regulations is currently somewhat uncertain, and legal factors are often mentioned as critical factors and obstacles for self-driving cars. However, progress towards AVs is approaching though improved ADAS. Given the advantages of AVs, the government can encourage the introduction of AVs. As with electric vehicles, the government may offer, for example, tax incentives to manufacturers and buyers. The next legal challenge for AVs operations is the transmission of liability in case of an accident. As the driver of autonomous vehicles will have little or no control over the vehicle, liability will likely be transferred to the manufacturers. In order to not discourage manufacturers from producing self-driving cars, there are demands for the registration of vehicle crash data via EDR (electronic data recorders). This data can be used, for example, to determine who had the power to control the vehicle at the time of the accident. | [1,7,42,43,44] |
The higher number of sold passenger cars | Since there are statistics that the number of passenger cars sold is increasing, we have selected this challenge to be tested. More details can be found below. | [45] |
Moral issues | AVs sometimes must decide between two evils. For example, sacrifice itself and its passengers or running over pedestrians. One of the difficulties will be the definition of algorithms to assist autonomous vehicles in making moral decisions. In any case, these three potentially incompatible objectives must be achieved: not to discourage buyers, not to arouse public outrage, and to be consistent. Nevertheless, the achievement of these goals can lead to moral inconsistencies. Even if moral contradictions are very rare, algorithms must agree with human values. In order to find out which algorithms citizens would accept, a collective discussion on the ethics of AVs must be started. | [46] |
Research Questions | Related Hypothesis | Section of the Survey |
---|---|---|
RQ1: Are Austrians ready to adopt AVs as their daily driver? | H1, H3 | Section B: Current usage of cars and willingness to drive an AV |
RQ2: How long will it take for AVs to be on the roads in Austria? | H4 | Section E: Timeline |
RQ3: Are consumers ready to trust autonomous vehicle technology? | H2, H4 | Section C: Challenges of AVs |
RQ4: What is the consumers’ perspective on using AVs for car-sharing/pooling? | H5 | Section D: Ownership/sharing of AVs |
N | Minimum | Maximum | Mean | Std. Deviation | Variance | |
---|---|---|---|---|---|---|
Safety concerns | 192 | 1 | 5 | 3.84 | 1.028 | 1.058 |
Transition concerns | 192 | 1 | 5 | 3.77 | 1.079 | 1.165 |
Legal concerns | 192 | 1 | 5 | 4.08 | 0.978 | 0.956 |
Cost concerns | 192 | 1 | 5 | 3.68 | 1.068 | 1.141 |
Cyber concerns | 192 | 1 | 5 | 3.82 | 1.009 | 1.019 |
Valid n (listwise) | 192 |
Cronbach’s Alpha | Cronbach’s Alpha Based on Standardized Items | Number of Items |
---|---|---|
0.703 | 0.704 | 5 |
Safety Concerns | Transition Concerns | Legal Concerns | Cost Concerns | Cyber Concerns | |
---|---|---|---|---|---|
Safety concerns | 1.000 | 0.371 | 0.331 | 0.367 | 0.365 |
Transition concerns | 0.371 | 1.000 | 0.336 | 0.220 | 0.388 |
Legal concerns | 0.331 | 0.336 | 1.000 | 0.266 | 0.334 |
Cost concerns | 0.367 | 0.220 | 0.266 | 1.000 | 0.246 |
Cyber concerns | 0.365 | 0.388 | 0.334 | 0.246 | 1.000 |
Gender | N | Mean Rank | Sum of Ranks | |
---|---|---|---|---|
Concerns index | 0 | 77 | 82.52 | 6354.00 |
1 | 115 | 105.86 | 12,174.00 | |
Total | 192 |
Concerns Index | |
---|---|
Mann–Whitney U | 3351.000 |
Wilcoxon W | 6354.000 |
Z | −2.867 |
Asymp. Sig. (2-tailed) | 0.004 |
N | Minimum | Maximum | Mean | Std. Deviation | Variance | |
---|---|---|---|---|---|---|
Feeling of safety in a conventional car | 192 | 1 | 5 | 3.81 | 0.848 | 0.719 |
Feeling of safety in an AV | 192 | 1 | 5 | 2.64 | 1.083 | 1.174 |
Valid n (listwise) | 192 |
Concerns Index | The Feeling of Safety in a Conventional Car | |||
---|---|---|---|---|
Spearman’s rho | Concerns index | Correlation Coefficient | 1.000 | 0.038 |
Sig. (2-tailed) | . | 0.605 | ||
n | 192 | 192 | ||
The feeling of safety in a conventional car | Correlation Coefficient | 0.038 | 1.000 | |
Sig. (2-tailed) | 0.605 | . | ||
n | 192 | 192 |
Concerns_Index | The Feeling of Safety in an AV | |||
---|---|---|---|---|
Spearman’s rho | concerns_index | Correlation Coefficient | 1.000 | −0.580 |
Sig. (2-tailed) | . | 0.000 | ||
n | 192 | 192 | ||
The feeling of safety in an AV | Correlation Coefficient | −0.580 | 1.000 | |
Sig. (2-tailed) | 0.000 | . | ||
n | 192 | 192 |
City | N | Mean Rank | Sum of Ranks | |
---|---|---|---|---|
Concerns index | 0 | 123 | 99.64 | 12,256.00 |
1 | 69 | 90.90 | 6,272.00 | |
Total | 192 |
Concerns Index | |
---|---|
Mann–Whitney U | 3,857,000 |
Wilcoxon W | 6,272,000 |
Z | −1.051 |
Asymp. Sig. (2-tailed) | 0.293 |
Concerns Index | Conventional Cars Replaced by Self-Driving Ones | |||
---|---|---|---|---|
Spearman’s rho | Concerns index | Correlation Coefficient | 1.000 | 0.289 |
Sig. (2-tailed) | . | 0.000 | ||
n | 192 | 192 | ||
Conventional cars are replaced by self-driving ones | Correlation Coefficient | 0.289 | 1.000 | |
Sig. (2-tailed) | 0.000 | . | ||
n | 192 | 192 |
Frequency | Percent | Valid Percent | Cumulative Percent | ||
---|---|---|---|---|---|
Valid | 0 | 106 | 55.2 | 55.2 | 55.2 |
1 | 86 | 44.8 | 44.8 | 100.0 | |
Total | 192 | 100.0 | 100.0 |
Frequency | Percent | Valid Percent | Cumulative Percent | ||
---|---|---|---|---|---|
Valid | 0 | 106 | 55.2 | 55.2 | 55.2 |
1 | 86 | 44.8 | 44.8 | 100.0 | |
Total | 192 | 100.0 | 100.0 |
Frequency | Percent | Valid Percent | Cumulative Percent | ||
---|---|---|---|---|---|
Valid | 0 | 141 | 73.4 | 73.4 | 73.4 |
1 | 51 | 26.6 | 26.6 | 100.0 | |
Total | 192 | 100.0 | 100.0 |
Frequency | Percent | Valid Percent | Cumulative Percent | ||
---|---|---|---|---|---|
Valid | 0 | 153 | 79.7 | 79.7 | 79.7 |
1 | 39 | 20.3 | 20.3 | 100.0 | |
Total | 192 | 100.0 | 100.0 |
No. | Hypotheses | Status |
---|---|---|
H1 | Women in comparison to men will show a higher level of concerns to use self-driving cars. | Accepted |
H2 | The higher level of automation in cars—the safer you feel in them. | Accepted |
H3 | People who live in cities in comparison with people who live in the countryside are well aware of AV technology. | Rejected |
H4 | The higher concerns about AV technology—the lesser is consumers’ confidence and willingness to use the technology. | Accepted |
H5 | The higher level of automation in cars—the higher is the willingness to use the AV for car sharing. | Rejected |
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Wintersberger, S.; Azmat, M.; Kummer, S. Are We Ready to Ride Autonomous Vehicles? A Pilot Study on Austrian Consumers’ Perspective. Logistics 2019, 3, 20. https://doi.org/10.3390/logistics3040020
Wintersberger S, Azmat M, Kummer S. Are We Ready to Ride Autonomous Vehicles? A Pilot Study on Austrian Consumers’ Perspective. Logistics. 2019; 3(4):20. https://doi.org/10.3390/logistics3040020
Chicago/Turabian StyleWintersberger, Sophie, Muhammad Azmat, and Sebastian Kummer. 2019. "Are We Ready to Ride Autonomous Vehicles? A Pilot Study on Austrian Consumers’ Perspective" Logistics 3, no. 4: 20. https://doi.org/10.3390/logistics3040020