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Microsoft's Garage Project Road Runner: More Accurate Training for AI Algorithms

via:博客园     time:2018/4/6 21:31:07     readed:642

The future development trend of the automotive industry is automatic driving. In addition to traditional automobile manufacturers, many giants of science and technology represented by Waymo have also joined this revelry. They have invested a lot of manpower and material resources in vehicle prototype design and construction of road infrastructure to facilitate the testing and collection of valuable data and information. Although Uber's car accident and Tesla's recent auto accident caused public skepticism about the safety of autonomous driving, it did not prevent the company from further advancing the development of this technology.


One of the challenges of current autopilot technology is how to train AI (artificial intelligence) algorithms to make decisions and drive cars, which requires data collection from millions or even billions of miles of driving. This is exactly what the Microsoft Garage Project Project Runner wanted to solve.


According to information published in the Microsoft Garage Official Blog, Project Road Runner is a research project in the Microsoft Garage in Redmond, Washington. The project is mainly to help the autopilot industry, developers, and researchers to collect data to train AI through simulated reality. As the name suggests, it is close to a realistic road scene in a controlled simulation environment, which makes the test more flexible and minimizes the impact on the actual road.


The above picture from Microsoft's official blog

Since the project has no funds, the team was operating on a free simulator for remotely controlled cars and the Internet at the very beginning. However, they later turned to AirSim. This is an open source simulation platform developed by Microsoft Research Institute based on the Unreal engine. It is mainly used for drone research. AirSim is also an ideal platform for training autopilot algorithms to achieve more accurate data capture in real-life situations.

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