Semantic Segmentation with Carla Simulator

• Main Author: Malec, Stanislaw
• Format: Others
• Language: English
• Published: Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) 2021
• Subjects: autonomous vehicles; synthetic data generation; semantic segmentation; computer vision; carla simulator; Computer Sciences; Datavetenskap (datalogi)
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105287

Autonomous vehicles perform semantic segmentation to orient themselves, but training neural networks for semantic segmentation requires large amounts of labeled data. A hand-labeled real-life dataset requires considerable effort to create, so we instead turn to virtual simulators where the segmented labels are known to generate large datasets virtually for free. This work investigates how effective synthetic datasets are in driving scenarios by collecting a dataset from a simulator and testing it against a real-life hand-labeled dataset. We show that we can get a model up and running faster by mixing synthetic and real-life data than traditional dataset collection methods and achieve close to baseline performance.