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Finding success on an international track Student members of the University of Missouri's Formula SAE team are learning firsthand that international collaboration is the future of engineering. A vision. A car. A cause. New Mexico State Aggies are tough enough to race pink. CyberAuto Challenge helps close talent gap Having a supply of high-quality young engineers is key to beefing up the automotive industry's cyber defenses.

Taking on the Valeo Innovation Challenge Two Canadian teams-the University of Ottawa and the University of Waterloo-finished in the top 3 among about 1,000 other universities from around the world in this challenge devoted to spurring innovative ideas for transportation. Colorado State University designs fuel-cell plug-in hybrid system Competing in the EcoCAR2 competition using a Chevrolet Malibu, the Colorado State team designed a system that features a 15-kW polymer electrolyte membrane fuel cell system, an 18.9-kW•h/177-kW lithium-ion battery, and a 145-kW motor. Cal State Fullerton combines art and engineering Team's submission on its Formula SAE car won first place in the second annual Generation Auto video contest organized in part by SAE International. Collaborative research project leads to potentially swarming VTOL UAVs The AVIGLE VTOL (vertical takeoff and landing) unmanned aerial vehicle was developed via collaboration by a variety of entities for a variety of applications.

DoD to Deploy Thousands of Low Cost Autonomous Systems Under Replicator Program Top Productivity Improvement Tips for Manufacturing Turbine Discs FACE Technical Standard Offers MOSA Lessons for Safety-Critical Software in Any Sector Adamant: A Soon-to-be Open Source, Mission-Critical Flight Software Framework Written in Ada Benefits and Challenges of Direct-RF Sampling for Avionic Platforms More Airports Test RF as Counter Measure for UAS in Restricted Airspace Adapting U.S. Army Acquisition to Ensure the Reliability and Safety of Autonomous Vehicles This report presents several challenges that the U.S. Army will face in the transition to autonomous vehicles, challenges that are only magnified in the current acquisition environment with limited testing. Artificial intelligence algorithms introduce additional complexity, resulting in systems with a complex combination of human, machine, and autonomous controllers.