How IoT and machine learning can make our roads safer

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Ben Dickson is a software engineer and the founder of TechTalks. More posts by this contributor: Why it's so hard to create unbiased artificial intelligence How to facilitate the path to brownfield IoT development Why it's so hard to create unbiased artificial intelligence How to facilitate the path to brownfield IoT development Why it's so hard to create unbiased artificial intelligence The transportation industry is associated with high maintenance costs, disasters, accidents, injuries and loss of life. Hundreds of thousands of people across the world are losing their lives to car accidents and road disasters every year. According to the National Safety Council, 38,300 people were killed and 4.4 million injured on U.S. roads alone in 2015. The related costs -- including medical expenses, wage and productivity losses and property damage -- were estimated at $152 billion.


Why You Probably Won't See Autonomous Vehicles Dominating The Road For A Long Time

Forbes - Tech

What needs to happen before we see autonomous vehicles in regular use? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. I believe we're still a long way away from autonomous vehicles becoming ubiquitous. We need a lot more mileage (literally!) under our belts, and more experience with so-called edge cases. What I mean by that is experience with unexpected situations such as: a passenger jumping unexpectedly in front of a self-driving car, a hidden stop sign after a tight turn, or a scooter or biker weaving in and out of the bike lane. I'd also include here exposure to adverse weather or to roadways with small tolerances.


The Place of Machine Learning and Artificial Intelligence in the Automotive Industry

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When it comes to vehicles, dials and switches are used to control everything. As the automotive industry evolves, so do its norms. Today, we are rapidly moving towards a world of shared and self-driving cars. Automotive manufacturers implement a range of human-machine interface technologies (HMIs), including voice controls, interior-facing cameras, touch-sensitive surfaces, and smarter, personalized platforms. Voice control is among the most preferred interfaces with the most significant percentage of HMIs since it allows hands-free control and, therefore, less distraction from the road. Other examples include multifunctional controllers, touchscreens, and head-up displays. Autonomous driving has been the central concern of the automotive industry for quite some time. This revolutionary concept wouldn't be possible without the help of Artificial Intelligence.


How Technology Can Make Our Roads Safer

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From car accidents to bridge collapses, 1.25 million road fatalities occur across the world annually. Another 20 to 50 million people suffer road injuries per year, some resulting in lifetime disabilities. Road incidents are also the source of economic damage to the victims, their families and nations themselves as they generate medical treatment expenses and repair costs as well as reduce productivity. There's fear that without noticeable action, roads will become the seventh leading cause of death across the world by 2030. However, there's hope that with the help of emerging technologies such as artificial intelligence and Internet of Things, we can make our roads and bridges much more safer, save hundreds of thousands of lives, and spare billions of dollars in maintenance and repairs costs.


How IoT and machine learning can make our roads safer

#artificialintelligence

Ben Dickson is a software engineer and the founder of TechTalks. The transportation industry is associated with high maintenance costs, disasters, accidents, injuries and loss of life. Hundreds of thousands of people across the world are losing their lives to car accidents and road disasters every year. According to the National Safety Council, 38,300 people were killed and 4.4 million injured on U.S. roads alone in 2015. The related costs -- including medical expenses, wage and productivity losses and property damage -- were estimated at $152 billion.