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Mobility, Hyperlanes, Bullet Trains, and AI Autonomous Cars - AI Trends

#artificialintelligence

I feel the need, the need for Maglev speed. The Maglev has been considered the fastest commercial High-Speed Rail (HSR) line and whisks passengers at a breathtaking 267 miles per hour from the Pudong airport to the Longyang station in Shanghai, a distance just shy of 20 miles. Named the Maglev because it uses magnetic levitation, it has been a marvel since it first opened in 2004. There are other high-speed rail lines of a research nature that are faster than the Maglev but holds the top record for a commercial in-use line. Let's call high-speed rail lines a more flavorful name, bullet trains. Of course, a bullet train cannot really go as fast as a bullet (which travels around 1,700 mph), though if you are standing on the sidelines when a bullet train goes past it might seem like it is going over a thousand miles per hour. Those of us in the United States don't have many bullet train choices and the preponderance of bullet trains are found in Europe and Asia. If you hold your breath, you might get a chance to someday ride a bullet train in California. That's actually a funny statement because anyone that lives in California knows that we've been pining away to have a bullet train for quite a long time.


AI is reshaping transportation. Railroads can get on board or miss out

#artificialintelligence

The following is an opinion piece written by Ian Jefferies, president and CEO of the Association of American Railroads. Opinions are the author's own. The White House recently issued draft principles for governing the use of artificial intelligence across sectors, including transportation. While a recent report noted the guidance may be too vague to produce substantive benefits, the larger point is clear. Various forms of AI are here to stay and will only become more ubiquitous.


Towards a Framework for Certification of Reliable Autonomous Systems

arXiv.org Artificial Intelligence

The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article. Keywords: autonomous systems; certification; verification; Artificial Intelligence 1 Introduction Since the dawn of human history, humans have designed, implemented and adopted tools to make it easier to perform tasks, often improving efficiency, safety, or security.


Within 10 Years, We'll Travel by Hyperloop, Rockets, and Avatars

#artificialintelligence

Try Hyperloop, rocket travel, and robotic avatars. Hyperloop is currently working towards 670 mph (1080 kph) passenger pods, capable of zipping us from Los Angeles to downtown Las Vegas in under 30 minutes. Rocket Travel (think SpaceX's Starship) promises to deliver you almost anywhere on the planet in under an hour. Think New York to Shanghai in 39 minutes. As 5G connectivity, hyper-realistic virtual reality, and next-gen robotics continue their exponential progress, the emergence of "robotic avatars" will all but nullify the concept of distance, replacing human travel with immediate remote telepresence.


The Modern-Day Future

#artificialintelligence

On February 6, 2018, Elon Musk's SpaceX launched the Falcon Heavy rocket, the largest ever, from NASA's Kennedy Space Center. Its cargo was a Tesla Roadster, which is now orbiting the sun somewhere between Mars and the asteroid belt. Between Elon Musk's numerous companies and passion projects (SpaceX, Tesla, Solar City, the Hyperloop, the Boring Company), and the quickly proceeding advances in VR/AR/MR, genetics/cloning, blockchain, AI, 3D printing, and other fields, someone who was in a coma since 1998 and just woke up yesterday would be forgiven for thinking they had jumped a hundred years into the future instead of a mere 20. But then this person would actually get up and go out into the real world and see that mostly everything else is the same, aside from more traffic on the roads, more people in general, most of whom now carry miniature computers with them wherever they go that are more powerful than any desktop from the 20th century. Born in apartheid-era South Africa, he lived the first 16 years of his life in various towns, including Pretoria, moving back and forth between divorced parents.


The Future of Transportation

#artificialintelligence

Sengupta: Thank you so much for having me today. I'm really excited to be in San Francisco. I don't get to come here that often, which is strange because I live in Los Angeles, but I do like to come whenever I can. For my talk today, I'm going to talk about the future of transportation, specifically on the things that I worked on that I think are kind of the up and coming thing, the thing that I'm working on now and what's going to happen in the future. I think part of my career has always been about just doing fun and exciting new things and all my degrees are in aerospace engineering, ever since I was a little kid, I loved science fiction. I actually am a Star Trek person versus a Star Wars person, but I knew since I was a little kid that I wanted to be involved in the space program, so that's why I decided to go the aerospace engineering route and I wanted to build technology. I got my Ph.D. in plasma propulsion systems. Has anyone heard of the mission called Dawn that's out in the main asteroid belt? My Ph.D. research actually was developing the ion engine technology for that mission. It actually flew and got it to a pretty cool place out in the main asteroid belt looking at Vesta and Ceres. I did that for about five years and then I kind of felt like I had done everything I could possibly do on that front, from a research perspective. My management asked me if I wanted to work on the next mission to Mars. There's very few engineers in the space program who'd be like, "No, I'm just not interested in that." And they're like, "We want you to do the supersonic parachute for it."


Flying robo-taxis eyed for Bay Area commuters

#artificialintelligence

French inventor Frank Zapata grabbed headlines around the world this summer when he flew his hoverboard across the English channel from Pas de Calais, France, to the famous white cliffs of Dover. But Bay Area commuters may soon do Zapata one better by skimming above San Francisco Bay on autonomous, single-passenger drones being developed by a Peninsula start-up company with ties to Google. The automated drones are electrically powered, capable of vertical takeoff and landing, and would fly 10 feet above the water at 20 mph along a pre-determined flight path not subject to passenger controls. The drones' rotors are able to shift from vertical to horizontal alignment for efficient forward movement after takeoff. The company behind all this, three-year-old Kitty Hawk Corp., has personal financial backing from Google founder Larry Page, now CEO of Google's parent, Alphabet, who has long been interested in autonomous forms of transportation.


Trans-Sense: Real Time Transportation Schedule Estimation Using Smart Phones

arXiv.org Machine Learning

Developing countries suffer from traffic congestion, poorly planned road/rail networks, and lack of access to public transportation facilities. This context results in an increase in fuel consumption, pollution level, monetary losses, massive delays, and less productivity. On the other hand, it has a negative impact on the commuters feelings and moods. Availability of real-time transit information - by providing public transportation vehicles locations using GPS devices - helps in estimating a passenger's waiting time and addressing the above issues. However, such solution is expensive for developing countries. This paper aims at designing and implementing a crowd-sourced mobile phones-based solution to estimate the expected waiting time of a passenger in public transit systems, the prediction of the remaining time to get on/off a vehicle, and to construct a real time public transit schedule. Trans-Sense has been evaluated using real data collected for over 800 hours, on a daily basis, by different Android phones, and using different light rail transit lines at different time spans. The results show that Trans-Sense can achieve an average recall and precision of 95.35% and 90.1%, respectively, in discriminating lightrail stations. Moreover, the empirical distributions governing the different time delays affecting a passenger's total trip time enable predicting the right time of arrival of a passenger to her destination with an accuracy of 91.81%.In addition, the system estimates the stations dimensions with an accuracy of 95.71%.


Efficient Incremental Learning for Mobile Object Detection

arXiv.org Artificial Intelligence

Object detection models shipped with camera-equipped mobile devices cannot cover the objects of interest for every user. Therefore, the incremental learning capability is a critical feature for a robust and personalized mobile object detection system that many applications would rely on. In this paper, we present an efficient yet practical system, IMOD, to incrementally train an existing object detection model such that it can detect new object classes without losing its capability to detect old classes. The key component of IMOD is a novel incremental learning algorithm that trains end-to-end for one-stage object detection deep models only using training data of new object classes. Specifically, to avoid catastrophic forgetting, the algorithm distills three types of knowledge from the old model to mimic the old model's behavior on object classification, bounding box regression and feature extraction. In addition, since the training data for the new classes may not be available, a real-time dataset construction pipeline is designed to collect training images on-the-fly and automatically label the images with both category and bounding box annotations. We have implemented IMOD under both mobile-cloud and mobile-only setups. Experiment results show that the proposed system can learn to detect a new object class in just a few minutes, including both dataset construction and model training. In comparison, traditional fine-tuning based method may take a few hours for training, and in most cases would also need a tedious and costly manual dataset labeling step.


US DOT forms council to support emerging transportation tech

Engadget

Secretary of Transportation Elaine Chao has announced a council aimed at supporting transportation projects including hyperloops and self-driving cars. The Non-Traditional and Emerging Transportation Technology Council (NETT) hopes to make sure the Department of Transportation's complex structure of various administrations doesn't impede companies from deploying such tech. "New technologies increasingly straddle more than one mode of transportation, so I've signed an order creating a new internal Department council to better coordinate the review of innovation that have multi-modal applications," Chao said in a statement. The Department of Transportation has 11 administrations (including the Federal Aviation Administration and the Federal Transit Administration), each with their own processes and regulations. The council, chaired by Deputy Secretary Jeffrey Rosen, will give companies a central access point to talk about their ideas and proposals, and NETT could help streamline permit, approval and funding processes.