The road to fully autonomous vehicles is long and marred by technological challenges, from the cost of developing the technology and commercial expansion, all the way through public acceptance and safety concerns. And that's if people even want to give up driving in the first place. But there's something that can help this transition, that can bridge the divide between the autonomous believers and self-driving sceptics: remote-controlled vehicles. And no, we're not talking about toy cars, these are real, adult-sized, remotely-driven automobiles. In this piece, I'm going to explain how they work, the commercial and technological implications, and if they can help ease us into an autonomous future.

There are many examples of cases where access to improved models of human behavior and cognition has allowed creation of robots which can better interact with humans, and not least in road vehicle automation this is a rapidly growing area of research. Human-robot interaction (HRI) therefore provides an important applied setting for human behavior modeling - but given the vast complexity of human behavior, how complete and accurate do these models need to be? Here, we outline some possible ways of thinking about this problem, starting from the suggestion that modelers need to keep the right end goal in sight: A successful human-robot interaction, in terms of safety, performance, and human satisfaction. Efforts toward model completeness and accuracy should be focused on those aspects of human behavior to which interaction success is most sensitive. We emphasise that identifying which those aspects are is a difficult scientific objective in its own right, distinct for each given HRI context. We propose and exemplify an approach to formulating a priori hypotheses on this matter, in cases where robots are to be involved in interactions which currently take place between humans, such as in automated driving. Our perspective also highlights some possible risks of overreliance on machine-learned models of human behavior in HRI, and how to mitigate against those risks.

Automated vehicles are rapidly advancing in capability, altering the risks and liabilities ... [ ] traditionally associated with driving. Self-driving vehicles should ideally accomplish a few things: convenience for operators/owners of vehicles, cost reduction for commercial vehicles (no driver), and safer roads (fewer and less severe crashes). This last item, if true, will significantly lower the risks traditionally associated with driving. In fact, the removal of the driver fundamentally alters the liabilities that insurance companies have spent almost a century covering. As liabilities and risks shift, how vehicles are insured and the costs of that insurance will change, disrupting a $300B industry and creating opportunities for innovation. The US Department of Transportation rates a vehicle's ability to self-drive from Level 0 (none) to Level 5 (fully autonomous).

As automated vehicles receive more attention from the media, there has been an equivalent increase in the coverage of the ethical choices a vehicle may be forced to make in certain crash situations with no clear safe outcome. Much of this coverage has focused on a philosophical thought experiment known as the "trolley problem," and substituting an automated vehicle for the trolley and the car's software for the bystander. While this is a stark and straightforward example of ethical decision making for an automated vehicle, it risks marginalizing the entire field if it is to become the only ethical problem in the public's mind. In this chapter, I discuss the shortcomings of the trolley problem, and introduce more nuanced examples that involve crash risk and uncertainty. Risk management is introduced as an alternative approach, and its ethical dimensions are discussed.

Movement towards road vehicle automation has been potentially the most revolutionary development in transportation during 2012. Transport research institutes in many countries and several car manufacturers have reported progress. The pacesetter in driverless technology is Google Inc., working through Stanford University's Artificial Intelligence Laboratory. Google has a fleet of more than a dozen what it calls "self-driving cars" that by August, 2012, had completed some 500,000 kilometres of testing. "They've covered a wide range of traffic conditions, and there hasn't been a single accident under computer control," explained Google's official blog.

Driving on Interstate 495 toward Boston in a Ford Fusion one chilly afternoon in March, I did something that would've made even my laid-back long-ago driving instructor spit his coffee over the dashboard: I took my hands off the steering wheel, lifted my foot off the gas pedal, and waited to see what would happen. To a degree, the car was already driving itself. Sensors were busy tracking other vehicles and road markings; computer systems were operating the accelerator, the brake, and even the steering wheel. The car reduced its speed to keep a safe distance from the vehicle ahead, but as that car sped up again, mine did so too. I tried nudging the steering wheel so that we drifted toward the dotted line on my left.

Self-driving vehicles promised to revolutionize the transportation industry. Automated cars could reshape every industry that relies on the automobile, from car manufacturing, to taxis, limousines and trucking. By taking human beings--who aren't the drivers they think they are--out from behind the wheel, self-driving cars could also save millions of lives. Self-driving cars will also have a profound effect on the environment--but whether it's for better or for worse will depend on technological and policy choices that have yet to be made. Automated vehicles could reduce energy consumption in transportation by as much as 90%, or increase it by more than 200%, according to research from the Department of Energy (DOE).

Most of the technologies being developed will help reduce large numbers of traffic accidents, deaths and serious injuries.

The crash that killed a Tesla driver in Florida when his car struck a tractor-trailer may mark the world's first fatal accident in which a computer was at the wheel. The crash occurred when the truck turned left across the 2015 Model S Tesla's path and the car's autopilot failed to slow down. The deadly accident, which took the life of 40-year-old Joshua David Brown of Ohio and is the subject of a federal safety investigation that Tesla disclosed Thursday, is bound to raise a lot of questions about vehicle automation and the future of car travel. It may be tempting to describe this as a driverless car crash, but don't give in. There's a big difference between assisted driving technologies and full automation, and what we have here is the former. We'll get into that below, but let's start first with the nuts and bolts of the autopilot technology at the center of the crash.

The crash that killed a Tesla driver in Florida on Thursday when his car struck a tractor-trailer may mark the world's first fatal accident in which a computer was at the wheel. The crash occurred when the truck turned left across the 2015 Model S Tesla's path and the car's autopilot failed to slow down. The deadly accident, which took the life of 40-year-old Joshua David Brown of Ohio, is bound to raise a lot of questions about vehicle automation and the future of car travel. It may be tempting to describe this as a driverless car crash, but don't give in. There's a big difference between assisted driving technologies and full automation, and what we have here is the former. We'll get into that below, but let's start first with the nuts and bolts of the autopilot technology at the center of the crash.