According to foreign media reports, with the continuous development of technology, today's vehicles are generally equipped with built-in data storage devices, the capacity is usually 16 GB-64 GB, most of which are suitable for vehicle map storage and car infotainment Features. However, artificial intelligence technology, autonomous driving technology, cloud technology, and vehicle networking technology are developing. In the future, autonomous vehicles will have to seek large-capacity storage devices to provide technical and hardware support for the above-mentioned advanced in-vehicle functions.
Demand for expansion of car memory is highlighted
According to research results from industry organizations, autonomous vehicles will soon need ultra-large capacity storage devices (with a storage capacity of more than 1TB), which will assist intelligent driver assistance, speech recognition and gesture recognition, eye tracking, driving monitoring, and black box recording. (black box recording), cognitive capabilities (learning and analysis of driver preferences to enhance capabilities), workshop communication (V2V) and vehicle and infrastructure communication (V2I) and other advanced driver assistance functions provide support.
As time goes by, the capacity of global road data will continue to increase, and the transmission rate will also increase, so storage devices are also in need of continuous expansion. Due to the huge amount of data generated, how to use massive amounts of data and how to transform data into real-time intelligence and service value is particularly critical.
In order to properly handle sensor data, algorithms and real-time data information from the outside world, it is necessary to improve the computing power of the vehicle, because the amount of data capture and analysis will be larger and larger. This requires real-time processing of data, some of which need to be saved to a local storage device (ie, in-vehicle storage device) or uploaded to a cloud storage device.
The effect of artificial intelligence on the vehicle storage equipment of self-driving vehicles
To this end, the following will discuss artificial intelligence and its impact on the vehicle-mounted storage devices of self-driving vehicles from the three aspects of “autopilot, digital assistant, and network connection to the data centerâ€.
Although the above application fields have great influence on the in-vehicle storage devices, they are inseparable from the promotion and assistance of artificial intelligence (AI) technology. The application of artificial intelligence technology in automobiles has become a major technological development trend. Artificial intelligence technology is based on computer applications that ensure that in-vehicle systems perform related tasks like humans.
Artificial intelligence not only drives the future of autonomous vehicles, but is also used by various major companies in various fields to improve the automation of the vehicle and improve the driving experience. Driver assistance, digital voice assistants, deep learning capabilities, and network connectivity to data centers and in-vehicle infotainment systems are all inseparable from the support of artificial intelligence technology. In-vehicle applications can use artificial intelligence technology to achieve massive data collection, but require relevant parties to develop new local and cloud information storage and data management strategies.
Large capacity car storage device is a prerequisite hardware condition for autopilot
The self-driving vehicle uses sensors such as cameras, radars, and laser radars (light detections and ranges) to collect data of the surrounding environment of the vehicle, thereby providing assistance for the functions of steering, braking, and acceleration of the vehicle, and the data thereof. The acquisition rate is 750 MB/s. The on-board sensor reads the map data of the surrounding environment of the vehicle and compares it with the saved on-board map to draw a new real-time map, enabling the vehicle to identify obstacles that may appear on the travel path and to circumvent it. This series of processes will generate a large amount of data to complete the control of the vehicle, but to achieve the above objectives, the premise is to significantly expand the storage capacity of the vehicle storage device.
The in-vehicle system extraction will extract the compressed data, which will be used in comparison with the HD map to accurately locate the vehicle. Such maps require high-precision map data and lane markings, curbs, lane signs, etc., and the capacity of the map data is doubled. This type of information can be used to generate real-time actionable insights to complete navigation operations on the vehicle.
Certain data, such as the vehicle's associated "driving" data, may take up to several days or even months depending on local regulations, operator or original equipment manufacturer (OEM) specific requirements. Driving data records must be recorded every few seconds, while black box data associated with fleet vehicle monitoring is recorded every few days. The latter is typically used for auto insurance, predictive maintenance, and other purposes.
Due to the different functions of the data, the corresponding data storage requirements are also very different. If you need to upload driving data to the cloud server's storage, you need to also save a local copy and save it locally (or "local") storage device. Ultimately, ultra-large capacity in-vehicle storage devices are a prerequisite for implementing these features.
It is estimated that most of the autopilot vehicles in the future will be equipped with the latest wireless network and vehicular communication equipment, allowing passengers to browse websites, send and receive mail, and watch downloaded movies in their cars. If this type of data requires long-term storage, users can upload it to the cloud and save it.
However, the moving vehicle cannot ensure that the signal of the wireless network in the vehicle is always in good condition, and it is impossible to ensure that the vehicle communication can continue to operate smoothly. For this reason, the relevant data has to be stored in the in-vehicle storage device, which requires further expansion of the in-vehicle storage device.
Digital assistants, mechanical learning, and deep learning will continue to increase the amount of data
Digital assistants can perform artificial intelligence through algorithms to take autonomous driving to the next level, but the amount of data generated by the algorithm is quite amazing. Unlike smart personal assistants, the former only provides voice services for mobile devices, while the digital assistants help the vehicle's mechanical learning, making the vehicle system aware of the driver's personal preferences, interests, and driving style.
The system not only provides a corresponding driving experience based on personalized information, but also continuously enhances its knowledge reserve. By analyzing behavioral patterns to imitate human behavior, interpret realistic driving situations like humans, and even take over vehicles if necessary. Driving control.
Machine learning (ML) is actually a subset of artificial intelligence (can be understood as a "subset"). The main application of this technology is machinery or equipment, ensuring that it can think and act like humans. . With artificial intelligence, machines or devices can perform tasks like humans. Thanks to mechanical learning technology, machinery or equipment can “continue to learn†based on acquired data and deep learning (DL) practices to perform various types of artificial intelligence tasks. Deep learning helps break down various tasks into manageable modules and then mimic them so that the in-vehicle system can learn the execution and operation of related functions. The more you learn, the more data you generate and the amount of storage you need.
Increased data usage in vehicles means larger capacity local storage and a large number of cloud gateway buffer codes
With the continuous development of connected vehicle technology, users have also acquired new functional experiences. The in-vehicle systems of interconnected vehicles can be classified into the following categories: in-vehicle infotainment systems, road and traffic warnings systems, vehicle diagnostics systems, navigation systems, and the like. The in-vehicle infotainment system based on artificial intelligence provides drivers and passengers with functions such as sending and receiving mail, web search and application linkage with smartphones. All of the above functions can be realized by voice commands. For this purpose, the transmission speed must be several megabits/ Seconds, the specific value depends on the operation of the vehicle application.
With the continuous increase in the number of in-vehicle applications and the continuous development of the Internet of Vehicles, the data transmission speed and transmission efficiency between the local storage (in-vehicle storage device) and the cloud (server) must be greatly improved. Need more capacity local storage and a lot of cloud gateway buffer coding.
The important contribution of artificial intelligence to connected vehicles is also reflected in the safety of automobiles. With the help of workshop communication (V2V) technology, connected vehicles will be able to realize information and data intercommunication between vehicles and “inform†surrounding vehicles of their upcoming actions. The implementation of this technology needs to be based on wireless network technology.
For example, if the driver does not decelerate before the red light is on, the on-board system of the connected vehicle will alert the driver to prevent it from flashing red at the traffic intersection. In addition, connected vehicles can be interconnected by means of vehicle infrastructure technology (V2I) and road infrastructure such as traffic lights and traffic signs. As a simple example, before the red light comes on, the traffic light will “tell†the connected vehicle, switching to the red light, and the connected vehicle will decelerate.
New trends in the application of in-vehicle storage devices - flash memory devices
Storage devices are a key component of a car's overall solution and account for a large proportion of bill-of-materials (BOM). Vehicles are more and more like wheel-mounted data centers, often equipped with multiple on-board computers, interconnected by cloud technology. As a result, storage device optimization becomes critical and is used to ensure device performance and reliability. Several new in-car entertainment information systems have recently been introduced, some of which enable software upgrades and save upgrade files to local storage devices. In addition, the application software used in the in-vehicle infotainment system will also generate a cache file for the bandwidth occupancy of the network peak.
In order to improve the performance of storage devices, expand storage capacity, reduce latency, improve reliability and durability, many car companies have switched to flash memory devices for storage operating systems and advanced software applications to collect and analyze driving data records. Cloud communication provides caching (and bandwidth optimization), and backup data for storing in-vehicle infotainment system data locally.
Tests have proven that flash memory can meet the high-capacity needs of autonomous vehicles, offering a highly compact package that is smaller than a one-cent coin. With the continuous improvement of the complexity of artificial intelligence and self-driving vehicle on-board systems, due to the limited space of the car, the space utilization rate has been strict to the inch level, and the flash memory just meets the requirements of the system for its size, and can be equipped as much as possible in the vehicle. The space occupied by it.
Local on-board storage devices that support connected vehicles and autonomous driving must withstand the rigors of the interior environment and must ensure that their functions operate safely and reliably, and that the product has a long service life. In view of the different environmental differences in the operation of the vehicle
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