High Definition Map Dynamic and Static Data Association Method for Intelligent Driving Scenarios

Objectives In the intelligent driving scene, the collaborative calls of real-time dynamic information and static data of high definition maps can support the intelligent driving system to accurately reconstruct the road driving scenes, and make safe and efficient decisions for complex road environment and emergencies. Therefore, the correlation between dynamic data and static data is a key technique for achieving vehicle's intelligent path planning and decision-making.

Methods To solve the problem of weak real-time coupling of dynamic and static data in current high definition map model, we propose an association method of dynamic and static data within high definition map based on the association principles of dynamic and static data, and this association method depends and can be triggered by update mechanism upon custom attributes and specific geographic locations.

Results We propose a high definition map dynamic and static data correlation method and analyze the impacts on intelligent driving through different macro-, meso- and microscales driving levels to verify the method's validity.

Conclusions The establishment of the dynamic and static data association relationship of the high definition map not only supports the planning, decision-making and control of the auto-driving system, but also lays the foundation for the realization of safe, efficient and comfortable intelligent driving of vehicles.