Argo AI, an autonomous vehicle company, has released two high-definition maps and a dataset that contains information about the trajectories of 300,000 vehicles driving through Pittsburgh and Miami to support advancements in computer vision. The maps include geometric and semantic metadata, such as lane centerlines, lane direction, and the driveable area. Argo AI extracted the vehicle trajectories from over 1,000 driving hours, and the dataset includes left turns and lane changes. Researchers can use this data to teach autonomous vehicles to better anticipate the movements of other vehicles.
Teaching Autonomous Vehicles to Understand the World Around Them
Michael McLaughlin is a research assistant at the Center for Data Innovation. He researches and writes about a variety of issues related to information technology and Internet policy, including digital platforms, e-government, and artificial intelligence. Michael graduated from Wake Forest University, where he majored in Communication with Minors in Politics and International Affairs and Journalism. He received his Master’s in Communication at Stanford University, specializing in Data Journalism.
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