Vehicle navigation path planning method

Abstract

The invention discloses a vehicle navigation path planning method, which comprises the following steps of: reading a plurality of log trajectories of a vehicle, and dividing the log trajectories into a plurality of loss elements containing driving characteristics; establishing a three-dimensional clustering coordinate system about time, economy and comfort performance of the road in the log trajectory, performing clustering screening on the loss elements to obtain a driving feature vector value, and performing quantitative analysis on the driving feature vector value to obtain a personalized performance value of the road; and deducing a navigation path according to the personalized performance value of the road. The driving characteristics of the user are extracted based on the log trajectory of the vehicle to which the user belongs, and personalized path planning is carried out from the view of the user, so that personalized navigation can be realized, the navigation route conforming to the driving habit of the driver can be selected, the clustering effect caused by single navigation information is avoided, congestion is avoided, and the user experience is improved. Therefore, the traffic dispersion pressure is reduced.

Description

technical field [0001] The invention relates to the field of vehicle route navigation, in particular to a vehicle navigation route planning method. Background technique [0002] With the vigorous development of the modern automobile industry, the number of self-driving cars is growing day by day, and the traffic pressure on the road network is increasing day by day, which cannot meet the growing needs of users for driving comfort. The current navigation system uses the geographic information provided by the Global Positioning System (GPS) or the Beidou system and the electronic map to detect the location of the vehicle, and then provide popular "distance-turn" navigation services. However, a single navigation information will often lead to clustering effect, resulting in congestion, thereby increasing the pressure of traffic control, and it is difficult to meet the personalized travel needs of users. At the same time, most of the driving habits of users show a certain stage,...

AI Technical Summary

Problems solved by technology

However, a single navigation information will often lead to clustering effect, resulting in congestion, thereby increasing the pressure of traffic control, and it is difficult to meet the personalized travel needs of users. At the same time, most of the driving habits of users show a certain stage, and they are not always the same. of

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