Automated driving and car networking will merge, and a chip-level mapping solution can better connect these two services. This article refers to the address: http:// In conjunction with the car enterprise to achieve the automatic driving schedule in stages, the map merchants also define the map products in stages. Recently, NavInfo's CTO Dai Donghai updated the progress of high-precision maps in an interview: In June of this year, the company had the ability to support Level 2 national highway network coverage data and products to achieve mass production. The Level 3 map is being promoted according to the commercial plan in mid-2017. The Level 4 high-precision map has been pre-verified in two cities in Beijing and Shanghai, and some work has already been done. By the beginning of 2019, Map4 data of major cities will be provided. After giving a specific time plan, how to provide auto-driving products for car companies, how auto-driving will work with the car-connected business, and what changes have been made to NavInfo in business processes. Car cloud and Dai Donghai talked about some details. NavInfo new CTO Dai Donghai chip-level map solution In May of this year, NavInfo acquired MediaTek, a subsidiary of MediaTek, a provider of in-vehicle infotainment systems solutions. The two companies will cooperate to develop and produce low-power and cost-effective chips. The main function is to perform map and sensor related operations in the process of automatic driving, including processing the sensory data from the vehicle-side sensors for high-precision matching and decision-making. Road properties and feature information, etc. Specifically, in the automatic driving process, high-precision maps need to communicate with the sensor moments. On this chip, the data of the sensor and the high-precision map will be merged to complete the real-time sensing task. At the same time, when the sensor crowd gathers the map data, the deep learning algorithm written in the chip can analyze and process the data collected by the camera and the laser radar, and extract the strong information about the map such as traffic and traffic facilities to improve the automatic driving map. The deep learning algorithm involved in the chip, the deep learning laboratory of the NavInfo New Basic Technology Research Institute is being developed and improved by itself. Taking the identification of traffic signs as an example, Dai Donghai said that the recognition rate achieved by the algorithm has surpassed that of human beings, and it is basically fully automated. How to store high-precision maps Compared with the high-precision map participation in the fusion data operation, the industry said that the more challenging is how the high-precision map is stored in the car. The high-precision map includes the basic navigation layer, high-precision road network layer, real-time dynamic layer, and many related environmental influence factors. There are crossovers on the layer and the traditional navigation map, but it will add a lot of fine elements and some requirements for updating. The basic data that is not high will be stored locally. As the level of autopilot increases, on the basis of ensuring the clarity, we must also find ways to control the map data size within a reasonable range. The answer to Dai Donghai is that the storage capacity of the car end can support the storage of high-precision maps. The amount of data supported by NavInfo's new Level2 and Level3 autopilot is not large. As long as it increases by about 1/3 of the traditional navigation map data volume, it can cover the current highway network nationwide. After Level 4, the amount of data will increase, but the amount of data is not very large. Including data collected by the camera and lidar sensor crowdsourcing, the algorithm also performs vectorization to reduce local storage and operations. Although the data types collected by the lidar and the camera are different, because of the process of data vectorization, if the depot adds a laser point cloud based on the original camera solution, the logic and solution of the entire architecture can be continuously extended. Autopilot and car networking will merge According to Dai Donghai, in the Level 2-Level3 automatic driving, this chip will be placed on the car end, which is also used to support the operation of the NavInfo new car networking products. To the Level 4 level, as the amount of computing increases, a separate chip can be provided for the sensing data processing of the automatic driving according to the needs of the vehicle enterprise. It is worth noting that when Dai Donghai introduced this autopilot map service, he classified it as version 3.0 of the Internet of Vehicles. The data collected by the car-side sensor will be uploaded to the cloud after being processed locally. Some confirmed updates will be sent from the cloud to the local update map, and the map will be used to support the auto-driving task. Automated driving and car networking will merge, and a chip-level mapping solution will better connect the NavInfo new car networking and autopilot services. Throughout the process, NavInfo has also evolved from simple map data to data services for the entire process. Tool chain innovation facing graphics The reason for mentioning this is because Dai Donghai thinks this is as important as the autopilot business. He introduced to the car cloud, especially to support Level4 autopilot, the data collected by laser radar equipment and crowdsourcing will be more and more. It is not realistic to rely on manual processing for large amounts of data. It requires an automated intelligent system to participate. In the process of electronicization of some physical worlds, some automated methods of operation have gradually been added. For example, a deep learning algorithm can be used to identify information such as POI position points and signs in the collected material, so that the machine can replace the manual mark. If the application of deep learning in the self-driving car is to liberate the driver, the application in the field of maps is " Take the cartographer away from the mark." We can also see some changes from the company structure. In the new research institute of NavInfo, four laboratories of intelligent driving, deep learning, future navigation engine core and cloud service have been established. In addition to the algorithm for developing the perceptual part, the deep laboratory also uses the various aspects of deep learning and map operations for the intelligent upgrade of the tool chain. However, Dai Donghai also admitted that the change and update of the operation process is not a one-time process, but a process of gradual reform. After the deep learning recognition sign is recognized, it is necessary to further understand the meaning of the sign. The innovation of the tool chain inside the map is also gradually realized. "This is the same as the development of autonomous driving." Electric Motor For Compressor,Air Compressor Asynchronous Motor,Compressor Asynchronous Motor,Adaptable Coaxial Motor Jiangsu Hengchi Motor Technology Co., Ltd , https://www.hcemotor.com