Automatic driving safety scene meta-modeling method driven by spatio-temporal trajectory data

Abstract

The invention discloses an automatic driving safety scene meta-modeling method driven by spatio-temporal trajectory data, which is characterized in that the meta-modeling method of the spatio-temporal trajectory data is constructed based on an MOF meta-modeling system. High-quality automatic driving automobile spatio-temporal trajectory data is obtained through collection of space-time trajectory data, construction of spatio-temporal trajectory metadata, preprocessing of the spatio-temporal trajectory data and data quality evaluation, then a spatio-temporal trajectory data meta-modeling process based on MOF is constructed, and a spatio-temporal trajectory data meta-model oriented to automobile automatic driving is constructed; and the security scene is automatically instantiated based on a scene modeling language ADSML. The invention provides an automobile automatic driving safety scene meta-modeling method based on spatio-temporal trajectory data driving, which can effectively support a model driving and data driving collaborative development method, and provides a new way for scene modeling in the field of automatic driving.

Description

technical field [0001] The invention relates to a safety scene element modeling method, in particular to an automatic driving safety scene element modeling method driven by spatio-temporal trajectory data. Background technique [0002] In recent years, the rapid development of big data, artificial intelligence, autonomous driving, smart city, intelligent transportation and other fields has promoted the widespread application of new generation information technology. How to efficiently use the large amount of data generated is an urgent problem to be solved. Effective spatio-temporal fusion of spatio-temporal data generated by earth observation and public media data (such as city cameras, social media, personal activities, etc.), high-reliability modeling, big data processing, and spatio-temporal data applications for specific fields have become Research hotspots in the field of spatial information technology and big data applications. Especially the global navigation satell...

AI Technical Summary

Problems solved by technology

[0004] However, at present, the scene modeling methods and technologies for real spatio-temporal trajectory data still have deficiencies: the research on autonomous driving scene modeling mainly focuses on the design of the modeling scene, emphasizing on the simulation results, without a set of systematic models The modeling method of driver and data-driven collaborative development is inconvenient for the management of real data and the generation of high-quality data, and there are problems of lack of a readable scene description language and difficulty in formal verification (Formal Verification)

Although GeoScenario and Scenic are more concise and readable than other scene modeling languages, their description methods are not friendly enough for domain experts, and formal methods cannot be used to verify and analyze scenarios

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