In the past, the idea of "autonomous driving" and "machines replacing human drivers" seemed far-fetched. However, Intel is now making this vision a reality. With its strong presence in the autonomous driving field, Intel is not just taking small steps—it's moving forward with six key technologies that are paving the way for the future of mobility. The real challenge in driving lies not in skills or car performance, but in the driver’s ability to observe and react quickly.
When driving, the driver uses their eyes to scan the road and makes decisions based on what they see. In the age of autonomous vehicles, 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 make intelligent decisions. In essence, high-precision maps act as the "eyes" of the vehicle, telling it where it is, what the environment looks like, and how to reach its destination safely.
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 key enabler for real-time insights into road conditions and timely decision-making for self-driving cars.
HERE HD Live* maps leverage big data and machine learning to deliver highly accurate and up-to-date information. These machine-readable maps allow autonomous vehicles to predict upcoming turns and road conditions. Unlike traditional maps, which are static and two-dimensional, these maps are layered—on one level, they identify roads, lane markings, elevation, and street signs. On another, they act as a dynamic sensor map, constantly updated by data collected from autonomous vehicles as they drive. This continuous feedback loop allows for real-time corrections and updates, ensuring the map remains accurate and reliable.
Intel’s efforts in high-precision mapping are accelerating the development of autonomous driving. As the industry grows, so does the demand for more precise and detailed maps. This creates a cycle where better maps lead to better performance, and better performance leads to even more data being collected and refined.
Cloud-based reference models help compare sensor data with the surrounding environment in real time. In complex autonomous driving scenarios, there are dynamic maps in the cloud and short-term static maps stored in the vehicle. As the car drives, its sensors and cameras capture real-time images, which are compared against the cloud’s dynamic map. Any changes ahead are detected, analyzed, and sent back to the cloud. This process allows the vehicle’s local map to be updated continuously, creating a cycle of improvement.
Big data and machine learning play a crucial role in providing accurate, real-time traffic information. It’s not just about identifying current traffic conditions; it’s about using historical data alongside current road network data to make predictions. Machine vision also plays a key role, allowing the system to recognize unusual events, such as a person suddenly crossing the road. These anomalies are sent back to the server to create new training samples, improving the system over time.
The relationship between high-precision maps and autonomous driving is deeply intertwined. Without accurate maps, path planning and localization would be impossible. To steer an autonomous vehicle, you need a reliable "invisible hand"—and the high-precision maps developed by Intel and HERE are a critical part of that. They enable the vehicle to navigate confidently and safely, turning the concept of autonomous driving into a reality.
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