A System-driven Automatic Ground Truth Generation Method for DL Inner-City Driving Corridor Detectors

• URL: http://arxiv.org/abs/2207.11234v1
• Date: Wed, 20 Jul 2022 12:55:16 GMT
• Title: A System-driven Automatic Ground Truth Generation Method for DL Inner-City Driving Corridor Detectors
• Authors: Jona Ruthardt (Robert Bosch GmbH) and Thomas Michalke (Robert Bosch GmbH)
• Abstract summary: We propose an automatic labeling approach for semantic segmentation of the drivable ego corridor. The proposed holistic approach could be used in an automated data loop, allowing a continuous improvement of the depending perception modules.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data-driven perception approaches are well-established in automated driving systems. In many fields even super-human performance is reached. Unlike prediction and planning approaches, mainly supervised learning algorithms are used for the perception domain. Therefore, a major remaining challenge is the efficient generation of ground truth data. As perception modules are positioned close to the sensor, they typically run on raw sensor data of high bandwidth. Due to that, the generation of ground truth labels typically causes a significant manual effort, which leads to high costs for the labelling itself and the necessary quality control. In this contribution, we propose an automatic labeling approach for semantic segmentation of the drivable ego corridor that reduces the manual effort by a factor of 150 and more. The proposed holistic approach could be used in an automated data loop, allowing a continuous improvement of the depending perception modules.

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