Advances made in machine learning and autonomous vehicles require a tremendous amount of computing power. In fact, an autonomous car can be looked at as a data center of its own. The next generation of applications will need near-real-time response from computing systems and in order to process the data from self-driving cars, computing power is going to need to be pushed to network edges. Also world leading research and advisory company, Gartner, is predicting that by 2020 there will be a quarter billion connected vehicles on the road making connected cars a major element of the Internet of Things (IoT).
Recently Kal Mos, Vice President for Connected Car, User Interaction & Telematics at Mercedes-Benz Research & Development North America, discussed the importance of edge computing in autonomous vehicles. Making use of different techniques in order for car features to work without a connection and enabling artificial intelligence within cars is where development in edge computing comes into play. However, advancements in autonomous driving will experience challenges along the way to evolution. Associated variables to be considered in human capabilities of drawing upon years of experience with driving will prove to be interesting in witnessing the jump from lab artificial intelligence to edge artificial intelligence. For more insight from Kal Mos, read full article here.