{"id":22338,"date":"2020-01-31T14:18:22","date_gmt":"2020-01-31T13:18:22","guid":{"rendered":"https:\/\/www.intellias.com\/?p=22338"},"modified":"2024-04-29T12:52:22","modified_gmt":"2024-04-29T10:52:22","slug":"how-autonomous-driving-impacts-software-development-in-the-automotive-industry","status":"publish","type":"blog","link":"https:\/\/intellias.com\/how-autonomous-driving-impacts-software-development-in-the-automotive-industry\/","title":{"rendered":"How Autonomous Driving Impacts Software Development in the Automotive Industry"},"content":{"rendered":"

For the majority of consumers, autonomous cars are still a thing from exhibitions and promotional events. For software developers, however, autonomous driving is a reality, and developers know exactly how your brand-new car should work. Autonomous driving software\u00a0is now a differentiator on the competitive automotive market, and self-driving car engineers are worth millions. In smart cars, everything boils down to software. As a vendor of autonomous car technology, Intellias knows what it takes to run\u00a0autonomous driving development<\/a>\u00a0and how autonomous driving engineering\u00a0has changed in recent years to meet demand.<\/p>\n

Autonomous driving development\u00a0is changing the automotive industry<\/h2>\n

Why is everybody so excited about self-driving capabilities, you may ask, if they\u2019re still not here? True, driverless cars are yet to win over consumers, but we\u2019re talking about various levels of autonomy, and this technology is improving each day and is here to stay. While at the moment, our roads see only partially independent cars, the global market for autonomous vehicles is estimated<\/a> to reach $36 billion by 2025, with the US owning 29% of all driverless cars.<\/p>\n

The higher the level of autonomy in a vehicle, the more intricate the software it hides under the hood and the more specific the expertise the engineering team needs. A classification system introduced by SAE International<\/a> in 2014 recognizes six levels of autonomous car technology. At level zero, a driver is entirely in charge of the vehicle, with no assistance at all. At level one, the vehicle can control its own speed. We\u2019re now somewhere at level three and approaching level four, where the car can be in charge of many situations, warning and assisting the driver, who can occasionally take his or her eyes off the road.<\/p>\n

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As for automotive software development, a serious change has happened between level two and level three autonomy. Level three conditional automation presupposes that a driver can take their eyes off the road and simply confirm the vehicle\u2019s decisions. It\u2019s not assistance anymore, but rather independence that increases with use cases and situations.<\/p>\n

Still, the road from level three to level four autonomy isn\u2019t smooth. The requirements of level three push the limits of classic ADAS rule-based functions with if\u2013then conditions. Use cases in urban environments require decision-making capabilities close to those of a human. Therefore, self-learning systems based on artificial intelligence (AI) are becoming a key technology in the automotive field.<\/p>\n

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Find out how Intellias developed an ADAS solution to make an electric vehicle safer and more efficient.<\/p>\n

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The role of AI and machine learning in today\u2019s automotive landscape: It\u2019s all about data<\/h2>\n

Artificial intelligence in the automotive industry isn\u2019t only about autonomous driving; it\u2019s also about road safety and connectivity. All the artificial brain needs is data. Various IoT devices built into vehicles, from cameras to sonar, constantly produce volumes of information for AI systems to process. According to Intel<\/a>, a single connected car may generate about 40 terabytes of autonomous vehicle big data<\/a> over an eight-hour period. There\u2019s also demand for advanced infotainment systems and various in-car services, which is another need AI technology can cover in a modern autonomous vehicle.<\/p>\n

All in all, AI deployments can cover a whole bunch of use cases within the automotive field besides autonomous driving itself:<\/p>\n