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.
"Most current advanced driver assistance systems based on radar and cameras are not capable of accurately detecting and classifying objects – such as cars, pedestrians or bicycles – at a level required for autonomous driving," said Sachin Lawande, president and CEO of Visteon, a leading global cockpit electronics supplier. "We need to achieve virtually 100 percent accuracy for autonomous driving, which will require innovative solutions based on deep machine learning technology. Our Silicon Valley team, with its focus on machine learning software development, will be a critical part of our autonomous driving technology initiative." Visteon's recently opened facility in the heart of Silicon Valley will house a team of engineers specializing in artificial intelligence and machine learning. The center is located close to the West Coast offices of various automakers and tech companies, as well as Stanford University and the University of California, Berkeley – two of the leading universities for artificial intelligence and deep learning in the U.S. In addition to leading Visteon's artificial intelligence efforts, the Silicon Valley office will play a key role in delivering control systems, localization and vision processing – interpreting live camera data and converting it to information required for autonomous driving.
Yes, real people, responsible for getting their vehicle from point A to point B. Car companies and the tech companies they increasingly work with seemed to acknowledge this reality. The focus has clearly shifted away from the unrealistic expectations about fully autonomous cars being available to purchase in the near future, and toward valuable, practical and safety-focused enhancements to the driving experience. Earlier this month at the Consumer Electronics Show in Las Vegas, several vendors demonstrated "digital cockpits" that dramatically improve the displays and controls with which drivers interact. Additionally, chipmakers Nvidia and Intel discussed advances in assisted driving platforms, commonly known as ADAS (Advanced Driver Assistance Systems). A link has been posted to your Facebook feed.
We uncovered the following insights and trends: • Semi-autonomous vehicles are the stepping stone to fully autonomous vehicles. Most car manufacturers and technology companies have taken Tesla's lead and are offering features like self- parking, adaptive cruise control, emergency braking and semi-hands off driving in highway/interstate conditions. Semi-autonomous features help consumers become comfortable with the idea of robots taking the wheel.
The idea of self-driving cars has captured consumer imagination, but AI is having a much broader impact across the entire automotive industry. From design, manufacturing, and infrastructure to predictive maintenance, safety, and a slew of AI-enabled cockpit features, auto experiences are evolving and improving. Not surprising, on-device AI is the primary force driving this transformation. It's enabling compute-intense AI workloads, such as complex neural network models, to execute in real time with high accuracy. In addition, as chipset providers add advanced AI capabilities and waterfall them across product tiers, sophisticated use cases are not just for luxury vehicles, but for entry-level cars as well.