Artificial Intelligence (AI) helps the vehicle to take decision in complex environment. AI is utilized in automobiles industry for smart mobility. At present, automotive industry has employed advanced driver assistance system (ADAS) and with increase amount of embedded intelligent the industry is progressing towards semi-autonomous vehicle. AI enables real-time recognition of surroundings and automates the vehicle mobility, controls in-vehicle systems, and eventually prevents accident. The various applications of AI in automobile sector is road tracking, capturing driver's gesture and expression, passenger experience, fleet management, weather monitoring, predictive maintenance, location search, E-payment and in-vehicle system control.
Last week, the U.S. Department of Transportation and the National Highway Traffic Safety Administration released a policy statement about self-driving cars. U.S. Secretary of Transportation Anthony Foxx boiled it all down to this: "The self-driving car raises more possibilities and more questions than perhaps any other transportation innovation under present discussion. That is as it should be. Possessing the potential to uproot personal mobility as we know it, to make it safer and even more ubiquitous than conventional automobiles and perhaps even more efficient, self-driving cars have become the archetype of our future transportation." There's a lot more, and you can download the whole document here but the take-away you care about is that the government recognizes that fully self-driving cars are coming, and it's time to decide how they're going to work, not whether they're going to be allowed.
Artificial intelligence (AI) is the word on everyone's lips. But in the automotive industry today, many products and services being labeled as such are in fact reliant on a form of advanced analytics (evolving from conventional algorithms) that enables those features--for example, predictive maintenance in manufacturing. Theories of AI have existed since 1950. However, AI itself gained wider functional applicability only in the past few decades, with the rise of machine learning and deep learning. This has also been facilitated by advances such as improved algorithms and training methods, greater computing power, and the availability of large amounts of data in the cloud.
What if large groups of people could go beyond ridesharing – replacing traditional car ownership altogether through on-demand access to the cars they want: a convertible in the summer, an SUV for winter ski trips? What if driving skills could be computed as a score that warned us of bad drivers nearby – real time, on the road – also enabling navigation systems to offer safer alternative routes? Imagine if we could get rid of traffic jams and accidents altogether. Or how about if our cars picked up our groceries on their own -- and dropped us off at the airport like a self-contained limo service? What if automakers could subsidize our car purchases by working with telecommunications and other companies that want to capitalize on the lifetime revenue opportunity of a connected driver?
Self-driving vehicles move closer toward reality every day, but most of us will likely live through a few more presidential elections before driverless travel is routinely within reach through personal purchase (although availability through ride-hailing providers like Uber and Lyft could be here sooner). Even so, you're more likely to encounter such vehicles in your daily travel, because many states and cities are now serving as testing grounds for these rapidly advancing technologies, and the list of locations is growing steadily. Legislators in 29 states have passed laws related to autonomous vehicles (AV), and governors in 11 more states have issued AV-related executive orders. But regardless of where they're happening, there are two things to remember about these experiments: They are inescapably disruptive, and they are entirely necessary. Slightly more than half of U.S. adults are apprehensive about riding in a self-driving vehicle, citing safety concerns and a general lack of trust in the technology, according to a Pew survey in 2017.