In the past, the idea of "autonomous driving" and "machines replacing human drivers" seemed distant and impractical. However, Intel is changing that perception. The company's current efforts in the autonomous driving field are described as "not a thousand miles away." With six key technologies acting as "steps," Intel is helping others take the first major strides toward fully autonomous vehicles. The real challenge in driving lies not in skills or car performance, but in vision.
When we drive, our eyes are the primary tool for scanning the road and making quick decisions. As we enter the age of self-driving cars, the "machine driver" will collect and update massive data in real time, feeding it into high-precision maps. These maps then use big data and machine learning to analyze and make decisions. In this way, high-precision maps act as the "eyes" of the vehicle, providing accurate information about the surroundings, location, and route planning.
High-precision maps are essential for navigation, positioning, path planning, and control in autonomous vehicles, directly impacting efficiency and safety. The high-precision map developed by Intel and HERE is a critical component for real-time insights into road conditions and timely decision-making for autonomous vehicles.
HERE HD Live maps utilize big data and machine learning to deliver highly accurate and up-to-date information. These machine-readable maps help autonomous vehicles predict upcoming turns and road conditions. Unlike traditional maps, these are layered—on the ground level, they identify road networks, lane markings, elevation, and surrounding signs. At the top, they provide a sensor-based map that updates in real time as autonomous vehicles drive and capture new data.
This process involves continuous analysis and correction, allowing the system to adapt quickly to changes. Intel’s work in high-precision mapping is accelerating the development of autonomous driving, while the growth of the industry itself also fuels the need for more accurate maps. In the early stages, many features like coffee shops and gas stations were manually calibrated, but with high-precision maps, this task becomes much more efficient. Instead of requiring 2,400–2,500 people, only around 100 are needed for high-definition map configuration.
Big data and machine learning play a crucial role in delivering real-time, accurate information to autonomous vehicles. Real-time traffic isn’t just about identifying what has already happened on a road segment—it’s about using historical data alongside current road network information to make predictions. Machine vision also plays a key role, as even well-trained models can encounter unexpected scenarios, such as a person suddenly crossing the street. These cases are sent back to the server to create new samples, improving the system's ability to handle all possible situations.
The interplay between high-precision maps and autonomous driving is complex and interconnected. Without accurate maps, tasks like path planning and self-positioning would be impossible. To guide an autonomous vehicle effectively, the high-precision map developed by Intel and HERE serves as a vital supporting technology. It acts like an invisible hand, helping to steer the future of transportation.
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