Insights from TU Automotive 2018: How Realists and Dreamers See Autonomous Cars Differently

The Appen team exhibited at the June 6-7 TU Automotive 2018 conference in Detroit. TU Automotive is the world’s largest conference on automotive technology related to the connected car. The show hosts industry experts and features case studies about the future of autonomous vehicles.  A photo of the Appen conference booth Our team at TU Auto attended a variety of seminars, including one by Andrew Hart, Director of SBD Labs, a market research and consultancy firm. In his session, Hart discussed the advances in auto tech and the ability to optimize smart cities, urban mobility, and connected services. Hart spoke about two mindsets in the connected car community: the realists and the dreamers. As he explained, realists are motivated by a fear of being the first to make mistakes, while dreamers are motivated by a fear of finishing second in the race to bring autonomous vehicles to smart cities. While these two attitudes may find themselves in conflict, both must work together to make this technology a reality. When considering why dreamers and realists matter, there are three important questions to consider: 1. What is it that makes realists and dreamers different? 2. Where can realists and dreamers find common ground? 3. How can realists and dreamers work together successfully?
A man giving a presentation at a conference

Andrew Hart of SBD Labs presents on Realists vs Dreamers – How They See Autonomous Cars Differently and Why it Matters at TU Auto Detroit 2018

1. What is it that makes realists and dreamers different? A dreamer sees autonomous vehicles as a replacement for the human driver, while a realist sees this technology as a support system for the human driver. One way to identify a dreamer from a realist is to understand his or her background. Is the scientist more familiar with digital or physical spaces when it comes to work? In the digital world, there are fewer restraints and the timelines are typically in day and week-long cycles. In the physical world, change takes place over months and years. 2. Where can realists and dreamers find common ground? For dreamers and realists to work together, they must agree on fundamental truths. The first truth is that human safety must come before corporate profits. The second truth is that there is no one-size-fits-all solution. Different engineers must develop different solutions for different places. The infrastructure of each city is unique and therefore each must be approached with its own unique data solution. 3. How can realists and dreamers work together successfully? It is essential for dreamers and realists to communicate in a common language. In technological applications, data provides a foundation for progress. Autonomous vehicles, smart cities, and urban infrastructures will all be built on the findings from structured data. While there are differences between the two, both dreamers and realists can agree that the self-driving car is a matter of “when” not “if”. The future of vehicular mobility, connected services, and data science depend on realists and dreamers working together toward common goals. With Appen data solutions, dreamers and realists can work together to transform advanced driver-assistance systems (ADAS), autonomous vehicles, and smart-city technology from grand dreams into practical realities. Polaroid of Appen team in Appen booth We’ve spent the last 15 years developing our expertise in the automotive industry, working with leading Tier 1 suppliers and 7 of the top 10 global OEMs. With an experienced team located in Detroit, Appen has the resources on the ground to accelerate product development and testing workflow. We develop data for the automotive industry to improve self-driving vehicles, enhance voice recognition, analyze sentiment, and more. Keeping up to date on the latest trends in artificial intelligence and machine learning? Follow us on Youtube for video updates on industry news. Contact us to learn more about human-annotated data solutions for machine learning and artificial intelligence.
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