Autonomous Vehicles: Overviews


The Road Ahead for Autonomous Cars and Auto Insurance

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The death of a pedestrian who was struck by an autonomous vehicle in Tempe, Arizona, has brought fresh scrutiny to the accelerating development of self-driving cars. The accident on March 18 is bound to be studied exhaustively, both to determine fault and to assess and refine the overall safety of autonomous systems. According to accounts of the accident, the vehicle, outfitted to test Uber's autonomous driving system, struck a woman at night as she pushed her bicycle across a road outside of a designated crosswalk. Video of the crash, released by Tempe police, shows a woman emerging from a darkened area seconds before she was struck; in the same span of time, the safety driver looks down multiple times for reasons that aren't clear. Uber pledged its full cooperation in the unfolding investigation but has already reached a settlement with some of the victim's family members, while others have come forward, according to multiple news reports.


An AI that makes road maps from aerial images

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Gaps in maps are a problem, particularly for systems being developed for self-driving cars. To address the issue, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have created RoadTracer, an automated method to build road maps that's 45 percent more accurate than existing approaches. Using data from aerial images, the team says that RoadTracer is not just more accurate, but more cost-effective than current approaches. MIT professor Mohammad Alizadeh says that this work will be useful both for tech giants like Google and for smaller organizations without the resources to curate and correct large amounts of errors in maps. "RoadTracer is well-suited to map areas of the world where maps are frequently out of date, which includes both places with lower population and areas where there's frequent construction," says Alizadeh, one of the co-authors of a new paper about the system.


A new way to automatically build road maps from aerial images

MIT News

Map apps may have changed our world, but they still haven't mapped all of it yet. Specifically, mapping roads can be difficult and tedious: Even after taking aerial images, companies still have to spend many hours manually tracing out roads. As a result, even companies like Google haven't yet gotten around to mapping the vast majority of the more than 20 million miles of roads across the globe. Gaps in maps are a problem, particularly for systems being developed for self-driving cars. To address the issue, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have created RoadTracer, an automated method to build road maps that's 45 percent more accurate than existing approaches.


How Driverless Cars Could Disrupt The Real Estate Industry

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Pony.ai self-driving cars during a trial run in February in Guangzhou, China. Driverless cars could become a regular feature of the roads as early as April โ€“ at least in California, which has decided to allow fully autonomous vehicles to be tested on the roads (none of those pesky humans who have been present in test drives so far). Arizona has already become a fair-weather center for testing driverless vehicles, thanks in large part to the governor's support, and Uber announced last week that it has finished testing its self-driving trucks in Arizona and is now beginning to use them to move goods across the state. The British government launched a review last week of laws governing self-driving vehicles, with the aim of getting autonomous cars on the road by 2021, and other countries around the world are also experimenting with autonomous vehicles. Clearly a big step for the technology and automotive industries โ€“ no surprise that companies working on driverless vehicles include Google and Uber, as well as traditional automakers like Audi, BMW, Ford, GM, Volkswagen and Volvo โ€“ the advent of human-less driving could also redirect the traffic of our days, how we live our lives and get around.


A Review of Udacity's Self-Driving Car Nanodegree -- Third Term and Beyond!

@machinelearnbot

It's okay for me, since I did not put that much effort in it.The I feel bad for the other students though. So there are two electives, semantic segmentation, and functional safety. Functional safety is interesting but I chose semantic segmentation, because it is a coding project, the functional safety project is to write a document. I learned about the concept of functional safety, and functional safety frameworks to ensure that vehicles is safe, both at the system and component levels.


A Guide to Challenges Facing Self-Driving Car Technologists

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In the minds of many in Silicon Valley and in the auto industry, it is inevitable that cars will eventually drive themselves. It is simply a matter of how long it will take for the technology to be reliably safe.


A guide to AI image recognition

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Artificial intelligence is becoming a centralised part of our everyday lives, even if we don't realise it. In fact, half of the people who encounter AI don't know they are doing so.


Teaching Autonomous Driving Using a Modular and Integrated Approach

arXiv.org Artificial Intelligence

Autonomous driving is not one single technology but rather a complex system integrating many technologies, which means that teaching autonomous driving is a challenging task. Indeed, most existing autonomous driving classes focus on one of the technologies involved. This not only fails to provide a comprehensive coverage, but also sets a high entry barrier for students with different technology backgrounds. In this paper, we present a modular, integrated approach to teaching autonomous driving. Specifically, we organize the technologies used in autonomous driving into modules. This is described in the textbook we have developed as well as a series of multimedia online lectures designed to provide technical overview for each module. Then, once the students have understood these modules, the experimental platforms for integration we have developed allow the students to fully understand how the modules interact with each other. To verify this teaching approach, we present three case studies: an introductory class on autonomous driving for students with only a basic technology background; a new session in an existing embedded systems class to demonstrate how embedded system technologies can be applied to autonomous driving; and an industry professional training session to quickly bring up experienced engineers to work in autonomous driving. The results show that students can maintain a high interest level and make great progress by starting with familiar concepts before moving onto other modules.


Apple Patent For Self-Driving Cars Auto-Updates Road Maps Already Traveled On

International Business Times

New details regarding Apple's efforts in autonomous car technology were revealed in a patent published this week, spotted by Autoblog.


Robotics, Positioning and AI for Mining, Construction Safety and Autonomous Vehicles

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Researchers from our group at QUT and the Australian Centre for Robotic Vision have had six papers accepted to the upcoming Australasian Conference on Robotics and Automation to be held at The University of Technology Sydney. This year the conference trialed a dual submission process with the IEEE International Conference on Robotics and Automation, meaning work can be presented at both conferences but only published in the proceedings of one. The papers cover ongoing research in our lab spanning topics including robotics, positioning and AI for applications in mining, construction safety and autonomous vehicles. I'll give an overview here of the research we're doing, and a wrap up at the end. Despite very high safety standards, work sites of all varieties around Australia still cause large numbers of injuries and occasional fatalities.