Improve the positioning for highly automated driving – extend your sensor range of the environment model!

Inhalt:
This paper describes a novel approach to cope with driving scenarios in automated driving which are currently solved only by the driver’s control. The approach presented in this paper is currently being implemented as a prototype to be used in our test fleet. It combines techniques well established in robotics like Simultaneous Localization And Mapping (SLAM) as well as end-to-end protection and image compression algorithms with big data technology used in a connected car context. This allows enhancing the positioning of individual vehicles in their Local Environment Model (LEM). This is the next step to overcome current dependencies to in-vehicle sensors by using additional cloud-based sensor processing to gain information.