Map model based on automatic driving application scene and application method thereof

Abstract

The invention discloses a map model based on an automatic driving application scene and an application method thereof. The model comprises a geographic point cloud, a network model, a road model, a traffic model and an automatic driving behavior model. The geographic point cloud comprises an environmental element layer, a road element layer and a boundary element layer, wherein the network model comprises a public network model and an ad hoc network model, the road model comprises a mathematical element layer, a geographic element layer, an auxiliary element layer and a geographic code, and the traffic model comprises a dynamic traffic model and a static traffic model; the automatic driving behavior model comprises a truck automatic driving behavior model, a passenger car automatic drivingbehavior model, a car automatic driving behavior model and a special car automatic driving behavior model. According to the map model disclosed in the invention, traffic rules are embedded into intelligent driving, so that the map model is fused with the driving depth of the automatic driving vehicle, and the map model controls the automatic driving vehicle.

Description

Technical field: [0001] The present invention relates to an automatic driving map model and an application method thereof, in particular to a map model based on an automatic driving application scene and an application method thereof. Background technique: [0002] With the advent of the era of big data, people's production and life have higher and higher requirements for the operational efficiency and basic functions of transportation. attention. However, most of the current mainstream technical means are attached to the perception functions of the vehicle devices such as sensors, radars, and cameras. For example, self-driving parking, self-driving on specific roads and other single-vehicle smart technologies; or focusing on creating maps with higher precision and better rendering effects, such as layer production, scene map rendering, etc. These methods that rely too much on the vehicle or perception system map have not achieved true deep data fusion, and cannot meet peo...

AI Technical Summary

Problems solved by technology

These methods that rely too much on the vehicle or perception system map have not achieved true deep data fusion, and cannot meet people's requirements from the perspective of safety and production efficiency.

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