Autonomous Vehicles: Overviews

Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks Artificial Intelligence

The benefits of autonomous vehicles (AVs) are widely acknowledged, but there are concerns about the extent of these benefits and AV risks and unintended consequences. In this article, we first examine AVs and different categories of the technological risks associated with them. We then explore strategies that can be adopted to address these risks, and explore emerging responses by governments for addressing AV risks. Our analyses reveal that, thus far, governments have in most instances avoided stringent measures in order to promote AV developments and the majority of responses are non-binding and focus on creating councils or working groups to better explore AV implications. The US has been active in introducing legislations to address issues related to privacy and cybersecurity. The UK and Germany, in particular, have enacted laws to address liability issues, other countries mostly acknowledge these issues, but have yet to implement specific strategies. To address privacy and cybersecurity risks strategies ranging from introduction or amendment of non-AV specific legislation to creating working groups have been adopted. Much less attention has been paid to issues such as environmental and employment risks, although a few governments have begun programmes to retrain workers who might be negatively affected.

4 things business leaders should know as they explore AI and deep learning


In order to make educated decisions in this fast-moving field, all managers should have a basic understanding of AI. Here are four key facts that will give you an edge. AI systems learn from the data and feedback that they receive in response to their earlier decisions. Their predictions and actions are only as good as the data they have been trained on. This characteristic makes AI systems very different from traditional deduction- based programming.

Singapore Trial of Shore to Ship Deliveries - UAS VISION


Launching at Singapore port's Marina South Pier in quarter three 2018, Wilhelmsen Ships Service and Airbus will be piloting the delivery of spare parts, documents, water test kits and 3D printed consumables via Airbus' Skyways unmanned air system (UAS) to vessels at anchorage. With the signing of an MOU at maritime trade show Posidonia, the Maritime UAS project agreement covers a joint ambition to establish a framework for cooperation between the Parties, with the aim of investigating the potential deployment and commercialization of UAS for maritime deliveries use cases. Marking the very first time, the viability of autonomous drone delivery to vessels has been put to the test in hectic, real-world port conditions, Marius Johansen, VP Commercial, Ships Agency at Wilhelmsen Ships Service is confident with Airbus now onboard his agency team's long-term drone delivery aspirations will be fulfilled. "We are absolutely thrilled to be working with a forward thinking, industry leader like Airbus. When we announced last year that we were pursuing drone delivery, we were greeted with a fair amount of scepticism, but our collaboration with Airbus, shows we really do mean business".

An AI that makes road maps from aerial images


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

#artificialintelligence 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 – 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 – 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 – 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!


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


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


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 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.