A self-adaptive approach for curbstone/roadside detection based on human-like signal processing and multi-sensor fusion

Newly emerging, highly complex Advanced Driver Assistance Systems (ADAS) fuse the output of various system modules (e.g., lane detection, object classification). Such knowledge fusion is realized in order to gain additional information of the environment allowing for complex system tasks as path planning, the active search for specific objects and task-specific analysis of the environment. As part of our previous work, we realized a highly generic type of such ADAS using biological principles. The present contribution offers a novel approach for the detection of curbstones and elevated roadsides in inner-city that relies on biological principles taking inspiration from the human neural signal processing. The gathered results can be fused to an ADAS in order to improve the quality of various other system percepts and allow additional system tasks.