Yi Fang, a research assistant professor in the Department of Electrical and Computer Engineering and a faculty member at NYU Abu Dhabi, and Edward K. Wong, an associate professor in the NYU Tandon Department of Computer Science and Engineering, are developing a deep learning system that will allow self-driving cars to navigate, maneuver, and respond to changing road conditions by mating data from onboard sensors to information on HERE HD Live Map, a cloud-based service for automated driving. NYU Tandon is one of HERE's first university research and development partners in HERE HD Live Map. The researchers added that this precision is also important because automobiles connected to HERE's HD Live Map service will deliver data to the cloud on road conditions, traffic, weather, obstacles, speed limits, and other variables, allowing the service to upgrade nearly in real-time to reflect changing conditions. Another venture - headed by a cybersecurity research team in the Department of Computer Science and Engineering - is developing the first free, open-source method for automakers to secure software updates.
The technological challenges that must be addressed before autonomous cars can be unleashed onto the streets are quite significant. Using deep learning techniques, the computer can look at hundreds and thousands of pictures, e.g., an electric guitar, and start to learn what an electric guitar looks like in different configurations, contexts, levels of daylight, backgrounds and environments. Sitting behind all this intelligence are neural networks; computer models that are designed to mimic our understanding of how the human brain works. The following year there were of course multiple deep learning models and Microsoft broke records recently when its machine was able to beat their human control subject in the challenge.
Machine learning software agents isolate images of potential Russian covert elements agitating protests, cross referencing cell phone pictures posted on social media with police traffic cameras, and more sensitive collection platforms. While many commercial applications of artificial intelligence are based on identifying patterns and trends using big data, most military applications focus on autonomous systems. Existing artificial intelligence programs in the Department of Defense include Navy unmanned undersea and aerial vehicle programs such as the Low-Cost Unmanned Aerial Vehicle Swarming Technology (LOCUST), and Air Force/DARPA ventures such as the Gremlin anti-surface-to-air missile drone program. Concepts range from larger logistics convoys composed of one manned vehicle and a large number of autonomous vehicles to combat formations mixing manned and unmanned platforms.
Besides supporting internal customers in truck design and engineering, the analytics group uses advanced statistics and machine learning techniques to benefit its external customers. The model predicts failures for more than 40,000 combinations of diagnostic trouble codes (DTCs) by make, model and year of vehicle. When alerts are found for International trucks, its customer service group can address the problem directly with the fleet customer. The team used the technique to analyze the usage patterns of 100,000 vehicles by engine operating hours, miles, idling time, etc.