Artificial intelligence (AI) features are increasingly being embedded in cars and are central to the operation of self-driving cars (SDC). There is little or no effort expended towards understanding and assessing the broad legal and regulatory impact of the decisions made by AI in cars. A comprehensive literature review was conducted to determine the perceived barriers, benefits and facilitating factors of SDC in order to help us understand the suitability and limitations of existing and proposed law and regulation. (1) existing and proposed laws are largely based on claimed benefits of SDV that are still mostly speculative and untested; (2) while publicly presented as issues of assigning blame and identifying who pays where the SDC is involved in an accident, the barriers broadly intersect with almost every area of society, laws and regulations; and (3) new law and regulation are most frequently identified as the primary factor for enabling SDC. Research on assessing the impact of AI in SDC needs to be broadened beyond negligence and liability to encompass barriers, benefits and facilitating factors identified in this paper. Results of this paper are significant in that they point to the need for deeper comprehension of the broad impact of all existing law and regulations on the introduction of SDC technology, with a focus on identifying only those areas truly requiring ongoing legislative attention.
In the face of daily pandemic-induced upheavals, the notion of "business as usual" can often seem a quaint and distant notion to today's workforce. But even before we all got stuck in never-ending Zoom meetings, the logistics and transportation sectors (like much of America's economy) were already subtly shifting in the face of continuing advances in robotics, machine learning and autonomous navigation technologies. In their new book, The Work of the Future: Building Better Jobs in an Age of Intelligent Machines, an interdisciplinary team of MIT researchers (leveraging insights gleaned from MIT's multi-year Task Force on the Work of the Future) exam the disconnect between improvements in technology and the benefits derived by workers from those advancements. It's not that America is rife with "low-skill workers" as New York's new mayor seems to believe, but rather that the nation is saturated with low-wage, low-quality positions -- positions which are excluded from the ever-increasing perks and paychecks enjoyed by knowledge workers. The excerpt below examines the impact vehicular automation will have on rank and file employees, rather than the Musks of the world.
Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.
Driving is an intuitive task that requires skills, constant alertness and vigilance for unexpected events. The driving task also requires long concentration spans focusing on the entire task for prolonged periods, and sophisticated negotiation skills with other road users, including wild animals. These requirements are particularly important when approaching intersections, overtaking, giving way, merging, turning and while adhering to the vast body of road rules. Modern motor vehicles now include an array of smart assistive and autonomous driving systems capable of subsuming some, most, or in limited cases, all of the driving task. The UK Department of Transport's response to the Safe Use of Automated Lane Keeping System consultation proposes that these systems are tested for compliance with relevant traffic rules. Building these smart automotive systems requires software developers with highly technical software engineering skills, and now a lawyer's in-depth knowledge of traffic legislation as well. These skills are required to ensure the systems are able to safely perform their tasks while being observant of the law. This paper presents an approach for deconstructing the complicated legalese of traffic law and representing its requirements and flow. The approach (de)constructs road rules in legal terminology and specifies them in structured English logic that is expressed as Boolean logic for automation and Lawmaps for visualisation. We demonstrate an example using these tools leading to the construction and validation of a Bayesian Network model. We strongly believe these tools to be approachable by programmers and the general public, and capable of use in developing Artificial Intelligence to underpin motor vehicle smart systems, and in validation to ensure these systems are considerate of the law when making decisions.
There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.
This story was originally published by Undark and is reproduced here as part of the Climate Desk collaboration. Deep in the Mojave Desert, 60 miles from the city of Barstow, is the Slash X Ranch Cafe, a former ranch where dirt bike riders and ATV adventurers can drink beer and eat burgers with fellow daredevils speeding across the desert. Displayed on a wall alongside trucker caps and taxidermy is a plaque that memorializes the 2004 DARPA Grand Challenge, a 142-mile race whose starting point was at Slash X Ranch Cafe. It was the first race in the world without human drivers. Instead, it featured the fever-dream inventions -- robotic motorcycles, monster Humvees -- of a handful of software engineers who were hellbent on creating fully autonomous vehicles and winning the million-dollar prize offered by the Defense Department's Defense Advanced Research Projects Agency.
Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.
Beyond trendy names like Tesla and Alphabet chasing self-driving cars, a host of auto brands and other tech heavyweights are also investing in autonomous R&D. Private companies working in auto tech are attracting record levels of deals and funding, with autonomous driving startups leading the charge. Along with early-stage startups, VCs, and other investors, large corporations are also angling to get a slice of the self-driving pie. From autonomy to telematics to ride sharing, the auto industry has never been at more risk. Get the free 67-page report PDF. Using CB Insights' investment, acquisition, and partnership data, we identified over 40 companies developing road-going self-driving vehicles. They are a diverse group of players, ranging from automotive industry stalwarts to leading technology brands and telecommunications companies. This list is organized alphabetically and focuses on larger corporate players in the space (as opposed to earlier-stage startups). Companies working on industrial autonomous vehicles were not included in this analysis. A few of the companies or brands listed below belong to the same parent organization but are detailed separately if they are operating distinct autonomous development programs. Some companies are grouped together by key partnerships or alliances. Given the complex web of relationships between these players, other collaborations are also noted in each profile. This is not intended to be an exhaustive list of corporations working on autonomous vehicle technology. This brief was originally published on 9/25/2015 and featured 25 select corporations. It was updated and expanded on 5/17/2017, 9/4/2018, and 8/28/2019. Over the last decade, Amazon has spent billions of dollars working on finding ever-better solutions to the last-mile problem in delivery. It's built its own fleet of cargo jets, explored delivery by drone in the form of "Prime Air," and more. More recently, an increasing percentage of that investment has been directed toward autonomous vehicle technology. In February 2019, Amazon invested in Aurora Innovation, an autonomous tech startup run by former executives from two other firms with strong ties to self-driving technology: Google and Tesla. "Autonomous technology has the potential to help make the jobs of our employees and partners safer and more productive, whether it's in a fulfillment center or on the road, and we're excited about the possibilities." The Aurora investment isn't the only autonomous technology play that Amazon is pursuing. In January 2019, the company introduced the Amazon Scout, a six-wheeled electric-powered delivery robot.
Autonomous Vehicles (AVs) are increasingly embraced around the world to advance smart mobility and more broadly, smart, and sustainable cities. Algorithms form the basis of decision-making in AVs, allowing them to perform driving tasks autonomously, efficiently, and more safely than human drivers and offering various economic, social, and environmental benefits. However, algorithmic decision-making in AVs can also introduce new issues that create new safety risks and perpetuate discrimination. We identify bias, ethics, and perverse incentives as key ethical issues in the AV algorithms' decision-making that can create new safety risks and discriminatory outcomes. Technical issues in the AVs' perception, decision-making and control algorithms, limitations of existing AV testing and verification methods, and cybersecurity vulnerabilities can also undermine the performance of the AV system. This article investigates the ethical and technical concerns surrounding algorithmic decision-making in AVs by exploring how driving decisions can perpetuate discrimination and create new safety risks for the public. We discuss steps taken to address these issues, highlight the existing research gaps and the need to mitigate these issues through the design of AV's algorithms and of policies and regulations to fully realise AVs' benefits for smart and sustainable cities.