Traffic sign data processing method, electronic device and storage medium

Abstract

The invention discloses an electronic device, a traffic sign data processing method and a storage medium. The traffic sign data processing method comprises the steps of: acquiring collected traffic sign data, wherein the traffic sign data comprises vertex set data corresponding to each traffic sign board, and the traffic sign boards comprises zebra crossings; calculating a central point of each traffic sign board in the plurality of traffic sign boards; calculating a first distance from the central point of each traffic signboard to a preset vertex of the traffic signboard where the traffic signboard is located to obtain a first distance corresponding to each traffic signboard; screening zebra crossing data out from the traffic sign data, and determining a zebra crossing set, wherein eachzebra crossing datum in the zebra crossing set comprises vertex set data of zebra crossings, center points of zebra crossings and first distances corresponding to the zebra crossings; and determiningthe processed zebra crossings based on the zebra crossing set. The traffic sign data processing method can achieve the automatic detection of the overlapping, redundancy or position errors of the traffic signs, so as to improve the map making precision.

Description

technical field [0001] The invention relates to the field of automatic driving, in particular to a traffic sign data processing method, an electronic device and a storage medium. Background technique [0002] In the field of autonomous driving, the drawing of crowdsourced high-precision maps usually requires the construction of semantic geometry layers first. The drawing cost of crowdsourcing collection vehicles is low, so crowdsourcing collection vehicles are usually used to collect map-related data, and they will be extensively arranged to collect high-quality data and increase the update frequency of high-precision maps. At the same time, due to the low accuracy of crowdsourcing collection data , the distribution of traffic signs is complex, and it is inevitable that misjudgment will occur when the program processes traffic sign data when constructing a semantic geometry layer, resulting in overlapping, redundant or wrong position of traffic signs. These errors require m...

AI Technical Summary

Problems solved by technology

The drawing cost of crowdsourcing collection vehicles is low, so crowdsourcing collection vehicles are usually used to collect map-related data, and they will be extensively arranged to collect high-quality data and increase the update frequency of high-precision maps. At the same time, due to the low accuracy of crowdsourcing collection data , the distribution of traffic signs is complex, and it is inevitable that misjudgment will occur when the program processes traffic sign data when constructing a semantic geometry layer, resulting in overlapping, redundant or wrong position of traffic signs

These errors require manual visual inspection of the results of traffic sign fusion processing. The overlapping, redundant or wrong position of traffic signs is very time-consuming and easy to miss.

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