Autonomes Fahren: Wie sieht die Nachfrage aus?

Wieviel Nachfrage besteht nach autonomen Fahrzeugen? Das hat eine Studie untersucht.

Ben Levine: Your project focuses on modeling people’s adoption of autonomous vehicles, which seems to mix the practical reality that autonomous vehicles are coming with the complex issue of how potential users will actually react to and use the technology. Can you talk about that dynamic?   
Sabya Mishra: You are right that connected autonomous vehicles (CAVs) are about to become a reality, and they are arriving much earlier than many would think. By incorporating features such as parking assist, adaptive cruise control and collision avoidance systems, most automobile manufacturers have already incorporated some degrees of automation into the existing cars. Mercedes-Benz, Google, Tesla and others have already developed and tested prototypes of the first fully autonomous vehicles. By aggressive testing of autonomous peer-to-peer ridesharing services, transportation network companies such as Uber and Lyft, are also pushing the introduction of automation.
One key question that has been of interest to policymakers, academic researchers and industry professionals is how much demand there will be for ownership of CAVs and what will be the timing of adoption in the long term? Our project attempted to address this question by collecting survey data, and applying Diffusion of Innovation theory and with agent-based modeling to forecast CAV adoption. 
Mike Rodriguez: The city of Memphis remains focused on decreasing traffic congestion and encouraging public transportation by providing affordable options. CAVs can support these efforts if we are proactive in developing a policy framework that provides guidance and reduces potential barriers. This project serves to provide greater insight into the individual demand and the forecasted adoption timeline. With these insights we can better prepare for infrastructure modifications and/or upgrades. Additionally, it highlights an opportunity to create a network to provide low-income citizens transportation services to and from medical visits, which frees up our emergency vehicles.
Levine: Can you share more about the idea of Diffusion of Innovation?
Mishra: The theory of Diffusion of Innovation seeks to understand how an innovation will diffuse as a result of communication and consumer interactions in a social network. It considers innovation diffusion as a social phenomenon that has four aspects: 1) the demand to adopt the innovation; 2) communication through certain channels; 3) communication among individuals in a social network; and 4) communication over time.
Diffusion research has been of interest to the academic community for an extended period of time. The seminal diffusion study was published in 1943, when Bryce Ryan and Neal Gross modeled the diffusion of hybrid seed corn among Iowa farmers.
Everett Rogers, a professor of communication studies, and others then extended the DOI theory application in many disciplines such as health care, computer science, engineering technology, agriculture, information technology, electrical and electronics engineering, etc. Our study extends its application to study the adoption of autonomous vehicles.
Levine: What do you think is embedded in someone’s consideration of adoption of connected and autonomous vehicles?  
Mishra: Well, this is really what our analysis looked at. To give some examples, we conducted a survey that resulted in some interesting findings. For example, less than 5 percent of respondents state that their households are willing to pay an additional $20,000 to add automation and connectivity; 69.1 percent of respondents' households are willing to pay only an additional $5,000 or less to have the driverless option added to their car. We found that improving social status among peers is the least important incentive to adopting CAVs, and the risk of virus attack is considered the most important barrier to adoption. One very interesting finding was that the losing feeling of control was more important than safety concerns for people. 

Figure 1: The graph above shows the Resistance to Self-Driving Car Adoption. Courtesy of Sabya Mishra.
Then, embedded in our analysis — and DOI modeling generally — is the notion that a person’s choices will have an impact on the choices of others. This conceptually makes sense — think about how your familiarity or comfort with a technology is shaped by those around you. In particular, we look at how positive and negative perceptions about CAVs will actually have an impact on somebody else’s willingness to pay for the technology. This modeling is undergirded by the concept of homophily, which indicates that individuals with geographical proximity and socioeconomic similarity are more likely to impact each other.  

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