Analysis of high-precision map application scenarios for autonomous vehicles

In order for self-driving cars to drive safely in complex traffic environments, accurate, real-time, and comprehensive environmental information acquisition is essential. The real traffic environment is ever-changing. Only by allowing self-driving cars to better understand the surrounding environment such as road signs, route planning, lane lines, traffic lights and obstacles, and making decisions in the changing surrounding environment, can the driving safety of self-driving cars be effectively improved. In order to allow self-driving cars to obtain more traffic environment information, in the design of self-driving cars, it is proposed to use perception hardware such as lidar, millimeter-wave radar, and on-board cameras to make self-driving cars see more clearly. However, due to the limitations of perception hardware, in complex traffic environments such as extreme weather and congestion, the road information obtained by perception hardware is not enough for self-driving cars to complete driving alone. For this reason, the solution of high-precision map-assisted self-driving has been proposed.

As a digital map, high-precision maps contain rich traffic environment information such as road geometry, traffic signs, traffic lights, pedestrians, buildings and obstacles. Unlike ordinary maps, high-precision maps provide richer and more accurate traffic environment information. In addition, high-precision maps can also help vehicles improve the accuracy and stability of positioning and navigation. The establishment and use of high-precision maps are crucial to the feasibility and safety of autonomous driving technology. High-precision maps are an important support for the development of autonomous driving technology.

High-precision map technology solution The establishment of high-precision maps requires the collection and processing of a large amount of data to generate map data, which mainly includes three parts: map data collection, map data processing and map data application.

Map data collection. Map data collection is the first step in high-precision map technology. Commonly used methods include the fusion of multiple sensors such as lidar, vehicle-mounted cameras and high-precision positioning to collect data from the environment. In the process of data collection, it is necessary to ensure the accuracy, comprehensiveness and real-time nature of the data. Therefore, when collecting data, it is necessary to consider multiple factors such as the location of the vehicle, the accuracy of the sensor, environmental factors and data quality. The collected data usually includes environmental information such as lane lines, intersections, traffic signs, traffic lights, obstacles, etc.

Map data processing. Map data processing is the process of processing the collected data and generating a high-precision map. In the process of data processing, multiple steps are required, such as data cleaning, feature extraction, map construction and data update. Among them, data cleaning refers to the removal of noise and invalid data to ensure the accuracy and consistency of the data; feature extraction refers to the extraction of feature information from the original data, such as lane lines, intersections and traffic signs; map construction is the conversion of the extracted feature information into map data, including road networks, lane lines, traffic signs and obstacles; data update refers to the addition of newly collected data to the map to ensure the real-time and comprehensiveness of the map data. The generated map data also needs to be stored for subsequent use. Common storage methods include cloud storage and distributed storage. As time goes by, the data of the high-precision map needs to be updated to ensure the accuracy and practicality of the high-precision map data.

Map data application. Map data application is the process of applying high-precision maps to assist the driving of self-driving cars. During the driving process of self-driving cars, high-precision maps can provide accurate location and route information to help vehicles navigate and make driving decisions. At the same time, the environmental information in the map can also help self-driving cars identify and avoid obstacles, identify traffic signs and signal lights, etc.

High-precision map application scenarios

High-precision maps are very important for self-driving cars. In addition to providing accurate traffic environment information to self-driving cars, they also have a wider range of application scenarios:

Provide traffic environment information. Self-driving cars need the assistance of high-precision maps to obtain more traffic environment information. With the support of high-precision maps, self-driving cars can understand the surrounding roads, traffic signs, traffic lights and other environmental information, so as to make corresponding driving decisions.

Provide the best driving route. High-precision maps can improve transportation efficiency and reduce traffic congestion. In addition to obtaining information about the surrounding traffic environment to ensure safe driving, self-driving cars also need to plan driving routes for travel. After entering the destination information, high-precision maps can help passengers plan the best driving route. If passengers want to have other stops during the journey or find that the established roads are congested, high-precision maps can make timely adjustments to optimize travel arrangements.

Provide reference for urban planning. High-precision maps can provide important information reference for urban planning because they have accurate urban environmental information. Through the information on high-precision maps, we can understand information such as urban transportation, public facilities, and crowded areas, providing important reference information for urban planning. In addition, high-precision maps can also monitor traffic flow in real time, warn of traffic accidents, and monitor vehicle violations, etc., providing more comprehensive data support for the construction of smart cities and smart transportation.

Advantages of high-precision maps

High-precision maps have very important advantages in the field of autonomous driving. By providing more accurate and comprehensive location information, road conditions information and environmental information, they help autonomous vehicles to better locate and navigate, improve the reliability and accuracy of driving decisions, reduce the difficulty of vehicle identification and obstacle avoidance, improve user experience, and reduce mapping costs, providing strong support for the commercial application of autonomous vehicles.

Improve positioning and navigation accuracy. High-precision maps can provide more accurate location and navigation information, helping autonomous vehicles to locate and navigate more accurately. Compared with traditional GPS positioning methods, high-precision maps can provide higher accuracy, usually between a few centimeters and more than ten centimeters. During vehicle driving, high-precision maps can provide more accurate location and direction information, helping vehicles to plan routes and make driving decisions more accurately. This is very important for autonomous vehicles, because they need to make accurate judgments and decisions on road conditions and environment to ensure safe and stable driving.

Improve the reliability and accuracy of driving decisions. High-precision maps can provide more comprehensive and accurate information about road conditions and the environment, helping autonomous vehicles make more reliable and accurate driving decisions. With the road condition information in the map, the vehicle can better judge the current driving status and make corresponding driving decisions. In addition, high-precision maps can also provide real-time traffic information, helping vehicles avoid congested roads and choose faster routes. This information is very important for the driving decisions of autonomous vehicles and can effectively improve driving safety and efficiency.

Improve vehicle recognition and obstacle avoidance capabilities. High-precision maps contain a variety of environmental information such as lane lines, intersections, traffic signs, obstacles, etc. This information can help vehicles better identify and avoid obstacles and improve driving safety. Through the lane line information in the map, the vehicle can more accurately determine the current lane, so as to better maintain and switch lanes. Through the obstacle information in the map, the vehicle can promptly detect and avoid obstacles ahead to avoid accidents. This information is very important for autonomous vehicles, which can improve the vehicle's recognition and obstacle avoidance capabilities, thereby ensuring driving safety and stability.

Improve user experience.

High-precision maps can improve the driving efficiency and safety of autonomous vehicles, thereby improving the user experience. Autonomous vehicles can choose faster routes and reduce driving time and traffic congestion through the real-time traffic information provided by high-precision maps. In addition, high-precision maps can provide more accurate estimated arrival times, helping users better plan their trips. All of these can improve users' travel experience and satisfaction, and enhance users' trust and acceptance of autonomous vehicles.

Reduce mapping costs. High-precision maps can obtain ground features and road conditions through satellite remote sensing technology, laser radar and other technical means, and then process and map data through cloud computing and other technical means. Compared with traditional manual mapping methods, high-precision maps can greatly reduce mapping costs and improve production efficiency, thereby providing better support for the commercial application of self-driving cars.