HD Lane Map Generation Based on Trail Map Aggregation
2022 IEEE Intelligent Vehicles Symposium (IV)(2022)
Abstract
We present a procedure to create high definition maps of lanes based on detected and tracked vehicles from perception sensor data as well as the ego vehicle using multiple observations of the same location. The procedure consists of two parts. First, an aggregation part in which the detected and tracked road users as well as the driving path of the ego vehicle are aggregated into a map representation. Second, lanes are extracted from those maps as lane center lines in a structured data format. The final lane centers are represented in a directed graph representation including the driving direction. They are accurate up to a few centimeters. Our procedure is not restricted to any environment and does not rely on any prior map information. In our experiments with real world data and available ground truth, we study the performance of different map aggregations e.g., based on the ego vehicle only or based on other road users. Furthermore, we study the dependence on the number of data recording repetitions.
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Key words
map representation,lane center lines,structured data format,final lane centers,directed graph representation,driving direction,prior map information,different map aggregations e.g,ego vehicle,HD lane map generation,trail map,high definition maps,detected tracked vehicles,perception sensor data,multiple observations,aggregation part,detected tracked road users,driving path
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