{"id":18631,"date":"2019-08-22T15:33:04","date_gmt":"2019-08-22T13:33:04","guid":{"rendered":"https:\/\/www.intellias.com\/?p=18631"},"modified":"2024-07-22T14:32:47","modified_gmt":"2024-07-22T12:32:47","slug":"what-does-unsupervised-learning-have-in-store-for-self-driving-cars","status":"publish","type":"blog","link":"https:\/\/intellias.com\/what-does-unsupervised-learning-have-in-store-for-self-driving-cars\/","title":{"rendered":"What Does Unsupervised Learning Have in Store for Self-Driving Cars?"},"content":{"rendered":"
Automotive engineers keep racking their brains about how to approach autonomous driving. OEMs keep giving multimillion-dollar injections to their R&D departments and partnering with tech companies and Tier 1 providers to increase driving autonomy. Meanwhile, self-driving must not only become reality but win people\u2019s hearts. And the key to winning hearts is providing high intelligence and safety, often with the help of machine learning in self-driving cars.<\/p>\n
Can machine learning in autonomous cars help manufacturers? Will self-driving car supervised learning or unsupervised learning algorithms make a difference in driving automation? Let\u2019s find out from our experts.<\/p>\n
In this article, you\u2019ll read about:<\/p>\n
Normally, it takes not just a month or even a year for a human to feel confident behind the wheel. So what can we expect from machines? Machine learning in self-driving cars powers the progress. A lot of learning. And machine learning, in turn, demands data to learn from. Autonomous driving desperately needs both machine learning algorithms and data to train them on. What\u2019s left for us humans is to provide that data while choosing the correct machine learning methods. We\u2019ve already started sorting out why machine learning algorithms are an integral part of autonomous driving<\/a>. To support this claim, let\u2019s look at unsupervised machine learning, a branch of artificial intelligence (AI) that helps machines learn effectively.<\/p>\n The term unsupervised learning refers to AI\/ML training models and is the opposite of supervised learning. Supervised learning algorithms rely on labeled input data and features of the learning environment. This way, the program predicts output based on data it has classified.<\/p>\n Unsupervised machine learning tries to score more points for artificial intelligence without any human touch. Unsupervised machine learning algorithms rely on data that has no labels, predefined features, or specified classification sets. Unsupervised AI\/ML systems learn from the deep-rooted structure of the input data.<\/p>\n There are plenty of unsupervised machine learning algorithms and many categories they can belong to. Unsupervised machine learning algorithms can be categorized according to the methods they use to group and process data.<\/p>\nJust in case you forgot what unsupervised learning is\u2026<\/h2>\n
Which are the most popular classes of unsupervised learning algorithms and which are used in driving automation?<\/h2>\n