Not enough data to create a plot.
Try a different view from the menu above.
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.
This ebook, based on the latest ZDNet / TechRepublic special feature, examines how 5G connectivity will underpin the next generation of IoT devices. Autonomous cars (and other vehicles, such as trucks) may still be years away from widespread deployment, but connected cars are very much with us. The modern automobile is fast becoming a sensor-laden mobile Internet of Things device, with considerable on-board computing power and communication systems devoted to three broad areas: vehicle location, driver behaviour, engine diagnostics and vehicle activity (telematics); the surrounding environment (vehicle-to-everything or V2X communication); and the vehicle's occupants (infotainment). All of these systems use cellular -- and increasingly 5G -- technology, among others. Although 5G networks are still a work in progress for mobile operators, the pace of deployment and launches is picking up.
The automotive industry is seen to have witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ high performance AI models to enable vehicles to perceive their environment and make driving decisions with little or no influence from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance to regulations. The assessment of these acceptance requirements can be facilitated through the provision of explanations for AVs' behaviour. Explainability is therefore seen as an important requirement for AVs. AVs should be able to explain what they have 'seen', done and might do in environments where they operate. In this paper, we provide a comprehensive survey of the existing work in explainable autonomous driving. First, we open by providing a motivation for explanations and examining existing standards related to AVs. Second, we identify and categorise the different stakeholders involved in the development, use, and regulation of AVs and show their perceived need for explanation. Third, we provide a taxonomy of explanations and reviewed previous work on explanation in the different AV operations. Finally, we draw a close by pointing out pertinent challenges and future research directions. This survey serves to provide fundamental knowledge required of researchers who are interested in explanation in autonomous driving.
Altran's parent, Capgemini, is combining the engineering skills of Altran with its data infrastructure and has launched a service for end to end support for validation and verification of driverless car systems. Altran also includes Cambridge Consultants in the UK which has been developing AI and sensor technologies for autonomous systems. The validation technology is being used by the maker of Citroen, Peugeot and Opel-Vauxhall cars to manage thousands of petabytes of data from testing the next generation of driverless cars. "We wanted to work with Capgemini and Altran because of their strong skills in data oriented and cloud- based projects. Participating in a European innovation project for the automotive industry in the field of the connected and autonomous car is very challenging. This collaboration enables us to complete our data collection and processing on schedule, and helps us to deploy innovative solutions for data analysis methods on a hybrid-cloud based solution," said Jean-Louis Sauvaget, Research & Development Division, Expert car data acquisition and post processing for customer usage in Groupe PSA.
Of course, operating systems have been present in cars for many years now, from the menus on the first digital stereos to the built-in in-car entertainment and satellite navigation systems that are offered as standard on almost every new car these days. However, these operating systems simply aren't future-proofed, and they don't manage the actual operation of the car itself – which we'll get onto later. Although there are already joint approaches between three (and more) of Germany's biggest automotive manufacturers to try and catch up with Tesla, there that BMW, Daimler and VW are working on a centralised operating system for driverless cars. So why is a collaborative operating system so important to the trio? In the next decade, there are two huge changes that automotive manufacturers face: the electrification of vehicles and the next level of autonomous driving that sees our control reduced either completely, or significantly.
In 2010, the small community of specialists who pay attention to US road safety statistics picked up the first signs of a troubling trend: more and more pedestrians were being killed on American roads. That year, 4,302 American pedestrians died, an increase of almost 5% from 2009. The tally has increased almost every year since, with particularly sharp spikes in 2015 and 2016. Last year, 41% more US pedestrians were killed than in 2008. During this same period, overall non-pedestrian road fatalities moved in the opposite direction, decreasing by more than 7%. For drivers, roads are as safe as they have ever been; for people on foot, roads keep getting deadlier. Through the 90s and 00s, the pedestrian death count had declined almost every year. No one would have confused the US for a walkers' paradise – at least part of the reason fewer pedestrians died in this period was that people were driving more and walking less, which meant that there were fewer opportunities to be struck. But at least the death toll was shrinking. The fact that, globally, pedestrian fatalities were much more common in poorer countries made it possible to view pedestrian death as part of an unfortunate, but temporary, stage of development: growing pains on the road to modernity, destined to decrease eventually as a matter of course.
The race to build fully autonomous cars has gone into hyper-drive, with major car-makers such as GM, Daimler, BMW and Audi promising SAE Level 5 autonomous driving by sometime in 2021. Goldman Sachs predicts that robo taxis will grow the ride-hailing and sharing business from $5 billion in revenue today to $285 billion by 2030. Autonomous driving will re-define mobility, and historic earning streams are sure to be toppled. Even with all the road testing the car-makers are doing, the only way the car companies can meet their ambitious goals is by leveraging the power of analytics and artificial intelligence (AI) to learn on real-world roads and accelerate development using simulations. The auto-makers are using simulation techniques such as hardware-in-the-loop (HIL) and software-in-the-loop (SIL) to make this happen.
This ebook, based on the latest ZDNet / TechRepublic special feature, advises CXOs on how to approach AI and ML initiatives, figure out where the data science team fits in, and what algorithms to buy versus build. It depends who you ask. Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. AI systems will typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity. AI is ubiquitous today, used to recommend what you should buy next online, to understand what you say to virtual assistants such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud. AI might be a hot topic but you'll still need to justify those projects.
More than half of parents struggle to keep up with the costs of the latest technology for their kids, it has emerged. Of 2,000 parents polled, one third admitted "going without" themselves in order to buy the latest products for their children. The study also found 37 per cent save all year to ensure their little ones have the same high-tech gadgets as their mates. How technology brought the #MeToo movement to India Over 75% of grandparents'learn about technology from grandchildren' Technology transforms how dogs sniff out poached African ivory But while eight in 10 parents feel'under pressure' to make sure their kid has the latest technology, seven in 10 have refused to buy brand new due to the sky-high price tags. And 38 per cent have opted for refurbished kit instead.
As the United Kingdom's largest automobile manufacturer and investor in research and development in the UK manufacturing sector, Jaguar Land Rover is the combination of two iconic British car brands--Jaguar that features luxury sports cars and sedans and Land Rover, maker of premium all-wheel-drive vehicles. These brands began in the middle of the 20th century and gained a reputation for innovation.