High-precision map-based cloud control automatic driving task generating method

Abstract

The invention discloses a high-precision map-based cloud control automatic driving task generating method. The method comprises the following steps: acquiring GPS information of a road by using centimeter-level high-precision positioning equipment, performing coordinate rotating conversion on a GPS track, performing curve fitting and selecting high-precision points to acquire the GPS sequence of a lane level of each road, establishing a high-precision map suitable for automatic driving and storing in a cloud server; and in the actual automatic driving stage, receiving an initial position and destination combined high-precision map and vehicle information in a global range from a vehicle by a cloud side, generating the route of the lane level, planning a driving task from the route of the lane level to form a task sequence, and issuing to the vehicle to perform execution and realize automatic driving. By the high-precision map-based cloud control automatic driving task generating method, hardware calculation resources of the internet and the cloud side are effectively utilized, and the complexity and the cost of the vehicle are reduced; and the cloud side masters the global traffic information, application of cloud processing, big data and artificial intelligent technology is benefited, and the automatic driving technology can be iterated and improved rapidly.

Description

technical field [0001] The invention belongs to the technical field of automatic driving, and in particular relates to a method for generating a cloud-controlled automatic driving task based on a high-precision map. Background technique [0002] With the further development of artificial intelligence and Internet of Things technology, the automobile and transportation industry is undergoing fundamental changes, and the research on intelligent transportation is also undergoing profound changes. In the past, the field of intelligent transportation research, which focused on the research of road traffic facilities, has been replaced by intelligent information platforms, precise positioning, precise maps, precise navigation, and intelligent vehicle communication technology. Autonomous driving research has become an indispensable part of the field of intelligent transportation research. , the most important and most concerned content. [0003] At present, most of the world's maj...

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Problems solved by technology

[0005] (1) The vehicle is realized by an integrated system, and various sensing devices, CPU, GPU and other hardware computing resources are integrated into the vehicle, which increases the complexity of the vehicle design; in addition, once a part of the vehicle fails, it will be a drag One shot moves the whole body, greatly increasing the maintenance cost and risk of the vehicle

[0006] (2) The vehicle needs to be equipped with a large number of sensors, and the requirements for the accuracy and real-time performance of the sensor equipment are high, which increases the weight and cost of the vehicle; at the same time, the vehicle is also equipped with a large number of hardware computing resources such as CPU and GPU. Increased vehicle energy consumption and computational costs

[0007] (3) The decision-making and control of the vehicle are completely dependent on the data obtained by the sensor, and there is not enough auxiliary information support from the cloud through the Internet. Even the high-precision visual sensor and radar sensor have limitations in applicable scenarios, resulting in Vehicle automatic driving is easily affected by the accuracy and time delay of these sensors, resulting in low vehicle reliability, limited applicable scenarios, and even affecting the driving safety of automatic driving

[0009] Today, with the prevalence of online car-hailing and car-sharing, most vehicles have become a mobile node on the intelligent transportation network. It is not meaningful to purely study the automatic driving of a single vehicle

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