Baidu L3 Business Unit uses high-precision maps to build autonomous vehicles
At the Baidu World Congress in September 2016, the L3 Smart Car Division was announced. Unlike Baidu ADU, the new department is taking a gradual development route, aiming at the L3 autonomous driving that is more commercially viable at this stage. Three months later, at the Baidu Yunzhi Summit in 2016, I met the L3 business unit again. With regard to L3 product development and business planning, we have some new understandings.
Map, maps, maps <br> <br> According to the introduction Gu Hao, general manager of Baidu dimension L3 Smart car division, L3 Division product architecture is the "13" mode. For L3 autopilot (positioning, perception, decision, control), car networking (CarLife, MapAuto, CoDriver, MyCar), automotive big data services (user analysis, CRM) three directions, the map is the basic support of the entire product architecture.
In L3's product architecture design, the map serves two roles, one is used as a sensing tool for self-driving cars, and is a map for self-driving cars; an interface used as a self-driving car to interact with people. It is a map that human drivers look at.
In autonomous vehicles, sophisticated high-precision maps can be used with sensors to achieve the vehicle's perception of the outside world. High-precision maps will write information such as lane lines, signal lights, signs, etc. that do not change frequently. If you fuse with other sensors while driving, you can reduce the burden of real-time operation of the sensor accordingly. For example, “there is a signboard with a speed limit of 60 on the right side of the road.†When driving to the vicinity, because the accurate position of the signboard is already in the high-precision map, the car camera can analyze the area with the signage in the picture in a targeted manner, confirming Speed ​​limit information, eliminating the need for full frame analysis to reduce the amount of calculation.
On the other hand, it is possible to use a mass-produced sensor such as a camera to match the high-precision map for precise positioning. Similarly, there is a signboard with a speed limit of 60 on the right side of the road. After the camera captures the speed limit card, it can match the position of the speed limit card in the map to determine the distance between the lane in which the vehicle is located or from the emergency stop. According to Gu Weijun, some car companies and the L3 business unit have already cooperated in this regard.
At present, Baidu's high-precision map coverage is more than 10,000 kilometers, including China's highways and urban expressways. The relative accuracy of 10-20 cm, the absolute accuracy of 60 cm, and the automation rate of up to 90% in the production process are the information given by Baidu to prove the high-precision map capability. The cooperation on high-precision maps is at the stage of Baidu providing sample data and assisting car companies to test and jointly develop. Baidu's high-precision maps are really applied to smart cars depending on the OEM's product planning, and the time may be in 2018.
Self-driving cars also need to communicate with people and need to give people a map. Zhang Hui, general manager of HMI & Ecology of Baidu L3 Business Unit, compares the experience of taking a self-driving car to a special car - enter the starting point and end, get a rest after getting on the bus, and you can know where it is when you hit it. As a function of high frequency use in the car, he believes that the map will become a human-machine interface. Our interaction with the car will be carried by the map, and voice will be the main way of interaction. Baidu car networking products are intended to use the mobile phone car interconnection program CarLife, the Baidu map car version into the car, through the car voice CoDriver to interact with the vehicle.
By car networking extending new map production model <br> <br> car networking products and autopilot package, one of the reasons is to pave the way Learning Map. When Gu Weizhen introduced the software service architecture, we can see that there is data exchange between the car networking products and LearningMap in the cloud.
LearningMap is not a new concept, it is the "crowdcast map" we often say. The road information is collected by the on-board sensor of the moving car, and the map data is supplemented and updated after the cloud is returned. The reason for this approach is that the cost of surveying vehicles is high. At present, there are only 10 vehicles in Baidu's 250 surveying and mapping vehicles that can be used for high-precision map data collection. The most important reason is high-priced equipment. On the other hand, in addition to the fineness of the high-precision map, it is also necessary to emphasize the "freshness" of the data, so that the map data update is contributed to the car that was originally running on the road, and the cost performance is the highest.
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