While this reality has become more tangible in recent years through consumer technology, such as Amazon's Alexa or Apple's Siri, the applications of AI software are already widespread, ranging from credit card fraud detection at VISA to payload scheduling operations at NASA to insider trading surveillance on the NASDAQ. Broadly defined as the imitation of human cognition by a machine, recent interest in AI has been driven by advances in machine learning, in which computer algorithms learn from data without human direction.1 Most sophisticated processes that involve some form of prediction generated from a large data set use this type of AI, including image recognition, web-search, speech-to-text language processing, and e-commerce product recommendations.2 AI is increasingly incorporated into devices that consumers keep with them at all times, such as smartphones, and powers consumer technologies on the horizon, such as self-driving cars. And there is anticipation that these advances will continue to accelerate: a recent survey of leading AI researchers predicted that, within the next 10 years, AI will outperform humans in transcribing speech, translating languages, and driving a truck.3
The terms "machine learning" and "artificial intelligence" (AI) conjure up feelings that are equal parts fear and fascination. Until recently, the prospect of a piece of software making human-like decisions resided safely in the far-fetched expectations of 1960s-era computer scientists or the plot lines of science fiction novels. Today, however, after decades of unmet expectations, we finally have AI systems that are beginning to influence our lives in tangible ways. Voice recognition systems like Amazon's Echo and Apple's Siri, and once-unimaginable fantasies like self-driving cars, are on the market for consumers, with more exciting life-like systems to come. We have also seen a few early signs of robotic autonomy that makes us feel uneasy, like the Russian robot that learned how to escape the lab!
Year to year, CES has a certain sameness about it: Intel's booth at the front, Sony's in the back and thousands of ginormous TVs in between. The topics and trends feel like the same things we've been talking about forever: Internet of things, smart home, autonomous vehicles, wireless everything. Is this really any different from last year?
We met up with Régis Vincent, Head of Software at SRI Robotics, a unit of SRI International, which is a non-profit, independent research centre serving government and industry. Located in Menlo Park at the heart of Silicon Valley, SRI International runs projects for government agencies, notably the Defense Advanced Research Projects Agency (DARPA) – the US Ministry of Defence agency which develops technologies for military use – as well as private sector players, both large firms and startups – intended to develop disruptive innovations. The stated aim of SRI International is to move R&D from the laboratory to the marketplace. SRI is hardly a household name, but the organisation has nevertheless been behind a large number of the devices which we now use in our daily lives. Since the research centre was founded 65 years ago, its engineers have been closely involved in the development of such innovations as colour television then colour photographic film in the 1950s, ultrasound for medical diagnostics in the 1980s, computers as we know them today, Arpanet, a 1960s precursor to the Internet, and more recently Siri – the first-ever virtual personal assistant, later acquired by Apple.
The impressive results of the 2007 DARPA Urban Challenge showed that fully autonomous vehicles are technologically feasible with current intelligent vehicle hardware. It is natural to ask how current transportation infrastructure can be improved when most vehicles are driven autonomously in the future. Dresner and Stone proposed a new intersection control mechanism called Autonomous Intersection Management (AIM) and showed in simulation that intersection control can be made more efficient than the traditional control mechanisms such as traffic signals and stop signs. In this paper, we extend the study by examining the relationship between the precision of cars' motion controllers and the efficiency of the intersection controller. We propose a planning-based motion controller that can reduce the chance that autonomous vehicles stop before intersections, and show that this controller can increase the efficiency of the intersection control mechanism.