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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>F1Tenth Autonomous Training Platform, Courseware and Community Activities</title>
      <link>https://rip.trb.org/View/2586804</link>
      <description><![CDATA[This is a continuation of a successful Safety21 project on developing a training community for engineering and ethical skills for developing future autonomous vehicles. This project includes three components - (1) autonomous driving course development with a 1/10th-scale autonomous racecar where students learn advanced algorithms and software development for perception, planning and control of autonomous driving; (2) Community Activities spanning 80 universities which have one or more F1tenth platforms and participate in the international autonomous racing competitions. The research team will host a minimum of 5 competitions in the top robotics, transportation and cyber-physical systems conferences; (3) Development of an ethical framework for using machine learning in life-critical systems.]]></description>
      <pubDate>Thu, 07 Aug 2025 14:10:52 GMT</pubDate>
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      <title>F1/10 Autonomous Racing Course and Competition</title>
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      <description><![CDATA[The course focuses on creating a meaningful and challenging design experience for graduate and senior undergraduate electrical engineering, computer science, mechanical engineering, robotics and embedded systems students. The course involves designing, building and testing an autonomous 1/10-scale model F-1 racecar (with 10 times the fun!) using the NVIDIA Jetson platform for real-time perception, control and planning. In addition to providing read-to-use material as a Teaching Kit, the course will introduce an autonomous racing competition in conferences at Embedded Systems Week 2016 and Cyber-Physical Systems Week with challenges testing speed, agility and tracking performance of the on-board vision and control algorithms.  Modern robots tend to operate at slow speeds when in complex environments, limiting their utility in high-tempo applications. In this course, students will be tasked with pushing the boundaries of unmanned vehicle speed, decision control and response to fast changes in the environment. Students will work in teams to develop autonomy software to race a converted 1/10 scale RC car equipped with sensors and embedded processing around a large-scale, “real- world” F-1 course. The project team's goal is to teach embedded GPGPU programming in a fun context of high-speed autonomous racing but with serious constraints of real-time processing, challenging controls and fast robot planning on the NVIDIA Jetson TK1 and TX1 platforms.]]></description>
      <pubDate>Thu, 11 Jul 2019 14:51:41 GMT</pubDate>
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