As technology advances, Autonomous vehicles are gaining attention. Autonomous vehicles generate about 1 GB of data per second, which includes sensory data, road condition information, location and data from surrounding vehicles. Massive amounts of data, increasing computing power, real-time operations, and security concerns have combined to accelerate the development of edge computing technology in the field of driverless cars. And major AI (artificial intelligence) technologies, such as deep learning, are being integrated into edge computing frameworks.
Not too long ago, the way we traveled changed drastically. Apollo Go, one of the pioneers in the field of Autonomous vehicles, is ushering in a new era in the way we travel. The close integration of unmanned autonomous driving technology and edge computing technology has made the way of traveling smarter, safer and more efficient.
Edge AI computing technology can take collected data that can be processed locally and can make decisions and predictions in real time without relying on remote resources. Edge computing platforms are only smarter and safer when they can host pre-trained deep learning models and have the computing resources to perform real-time reasoning locally.
Benefits of Edge Computing in AI Vehicles
Edge computing is well suited for bandwidth-intensive and latency-sensitive applications such as unmanned self-driving cars. With advances in in-vehicle communications and 5G Vehicular Networking (V2X), it is now possible to provide a reliable communication link between the vehicle and the infrastructure network (V2I).
Vehicle edge computing (VEC) systems, which need to compute large amounts of data in parallel, need to provide enough computing power to keep unmanned self-driving cars safe. This allows for instant processing of data even when the driverless car is traveling at high speeds.
1. Low Latency
Low latency is very essential. It has been investigated that it takes at least 150-200ms to transfer data back and forth in a network. Edge computing technology localizes the data and can shorten the data transfer and processing time.
2. Speed
For security reasons, much of the massive amount of data transmitted must be processed in the car. This requires a very high level of data computing power. Using edge AI technology to measure storage space can ensure that the CPU of the unmanned self-driving car can perform all computing tasks. This helps to reduce latency and improve accuracy.
3. Reliability
The safety of unmanned self-driving cars is critical. Edge computing reduces the pressure of cloud network congestion and provides better reliability by reducing data processing. Since edge computing and edge data centers are located in the car, data processing is less affected by network problems. Even in the event of a power outage in the data center, on-board intelligent edge computing in unmanned self-driving cars will continue to operate efficiently because they can process all data locally.
4. Security
An unmanned self-driving car needs to provide strong capability of data calculation to ensure its own security. The security of an unmanned self-driving car should cover all levels of the unmanned self-driving edge computing stack. This security includes sensor security, operating system security, control system stability and communication security.
5. Scalability
Automotive edge computing is essentially a distributed architecture that sends data to the edge of the network and analyzes it in real time. This reduces network latency during data transmission and the data no longer needs to be transmitted over the network to the cloud for processing.
Schlussfolgerung
Driverless systems are extremely complex. They tightly integrate many technologies and contain processes such as sensing, localization, and decision making. These complexities pose many challenges to the design of edge computing systems for unmanned autonomous driving.
It is believed that with the continuous integration of driverless and edge computing technologies, unmanned self-driving cars will guide us into a new era of mobility.