Collaborating Authors

RFID Camera Lock Smart Mailbox

IEEE Spectrum Robotics

A self-locking mailbox could someday flag down delivery drones and intelligently screen your driveway for intruders. Columbus State University computer scientist Lydia Ray presented the technology, called the ADDSMART project, during a 20 October session at the annual IEEE Ubiquitous Computing, Electronics, and Mobile Communication Conference in New York City. The project aims to achieve two goals: clearly marking addresses for autonomous vehicles, and reducing the energy and data storage costs of home surveillance systems. An early prototype mailbox attachment suggests that the trick, in both cases, may be radio-frequency identification. Powered by an Arduino Yun processor, one component of the ADDSMART device controls a high-frequency 13.56-MHz RFID reader, USB camera, passive-infrared motion sensor, solenoid lock, and an onboard Wi-Fi module.

Google tests air traffic control system that manages lots of drones


If you've been scratching your head at the FAA's extensive efforts to regulate your personal (or company) drone use, consider the chaos when they start filling the skies. That's why the agency partnered with NASA for a series of nationwide tests to explore potential systems that could track and manage a wide range of drones simultaneously. Google parent company Alphabet's Project Wing tried out its own UAV air traffic control platform yesterday, a system that might one day guide a massive volume of airborne drones to keep them from crashing into buildings, people or each other. Unsurprisingly, Project Wing's UTM (UAS Air Traffic Management) leans heavily on other Google products like Maps, Earth and Street View to navigate drones around obstacles and plan routes. During yesterday's tests, UTM managed flight paths for multiple UAVs simultaneously, according to the group's blog post.

A 20-Year Community Roadmap for Artificial Intelligence Research in the US Artificial Intelligence

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.

A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving Machine Learning

The models are updated using a CNN, which ensures robustness to noise, scaling and minor variations of the targets' appearance. As with many other related approaches, an online implementation offloads most of the processing to an external server leaving the embedded device from the vehicle to carry out only minor and frequently-needed tasks. Since quick reactions of the system are crucial for proper and safe vehicle operation, performance and a rapid response of the underlying software is essential, which is why the online approach is popular in this field. Also in the context of ensuring robustness and stability, some authors apply fusion techniques to information extracted from CNN layers. It has been previously mentioned that important correlations can be drawn from deep and shallow layers which can be exploited together for identifying robust features in the data.

The AI Dashcam App That Wants to Rate Every Driver in the World


If you've been out on the streets of Silicon Valley or New York City in the past nine months, there's a good chance that your bad driving habits have already been profiled by Nexar. This U.S.-Israeli startup is aiming to build what it calls "an air traffic control system" for driving, and has just raised an extra 10.5 million in venture capital financing. Since Nexar launched its dashcam app last year, smartphones running it have captured, analyzed, and recorded over 5 million miles of driving in San Francisco, New York, and Tel Aviv. The company's algorithms have now automatically profiled the driving behavior of over 7 million cars, including more than 45 percent of all registered vehicles in the Bay Area, and over 30 percent of those in Manhattan. Using the smartphone's camera, machine vision, and AI algorithms, Nexar recognizes the license plates of the vehicles around it, and tracks their location, velocity, and trajectory.