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

arXiv: https://arxiv.org/abs/2207.11234 · HTML: https://mlsanity.com/paper/2207.11234/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Enhancing Photorealism Enhancement](https://mlsanity.com/paper/2105.04619/) (2021)
- [Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving](https://mlsanity.com/paper/2202.07133/) (2022)
- [Synthetic-to-Real Domain Adaptation for Lane Detection](https://mlsanity.com/paper/2007.04023/) (2020)
- [Where can I drive? A System Approach: Deep Ego Corridor Estimation for Robust Automated Driving](https://mlsanity.com/paper/2004.07639/) (2020)

## Abstract (arXiv)

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.