# CarFree: Hassle-Free Object Detection Dataset Generation Using Carla Autonomous Driving Simulator
## 7 Conclusions  
This study's goal is to break the entry barrier for making training datasets of the object detection task in autonomous driving. Since gathering images with enough diversity and annotating their GTs require massive human labor, generating a good enough dataset is too costly. Our tool, CarFree, is based on the Carla autonomous driving simulator, under which we can freely simulate diverse driving scenarios and weather and lighting conditions. While the ego vehicle is driving around the virtual world, our developed Carla clients extract information from the Carla server later used by the post-processing GT generation program. Our GT generation method accepts the RGB images, the semantic segmentation images, and the 3D locations of vehicles and pedestrians in the simulation, and calculates the exact 2D location of each visible object in the forward-facing camera. All the process is fully automated, and the synthesized images are pixel-level precise for locating objects. Our evaluation results show that employing our synthesized images significantly improves the object detection performance of a state-of-the-art object detection model. Performance improvement is even more significant when the model is evaluated with test images exhibiting unusual weather conditions, which can significantly benefit the safety of autonomous driving systems operating in harsh environments.  
Figure 7: Evaluation of the training performance. At each result point, the mixture of KITTI and CarFree images is denoted by (number of KITTI images + number of CarFree images). (**a**) Evaluation results for usual driving conditions. (**b**) Evaluation results for harsh driving conditions.  
In the future, we plan to update CarFree to make it more practical and efficient. First, we plan to extend CarFree such that it can generate other object labels such as lane markings and traffic signals. Second, to increase the throughput of the image generation process, we plan to develop a new feature that deploys multiple ego vehicles by running multiple separate Carla clients in parallel.  
Conceptualization, J.J. and J.-C.K.; methodology, [PERSON]; software, [PERSON]; validation, J.J., H.L. and J.-C.K.; formal analysis, [PERSON]; investigation, [PERSON]; resources, J.J.; data curation, J.J., H.L. and J.-C.K.; writing--original draft preparation, J.J.; writing--review and editing, J.-C.K.; visualization, [PERSON] and [PERSON]; supervision, J.-C.K.; project administration, J.-C.K.; funding acquisition, J.-C.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported partly by the Korea Evaluation Institute Of Industrial Technology (KEIT) grant funded by the Ministry of Trade, Industry and Energy (MOTIE) (20000316, Scene Understanding and Threat Assessment based on Deep Learning for Automatic Emergency Steering) and partly by the Transportation Logistics Development Program (21 TLRP-B147674-04, Development of Operation Technology for V2X Truck Platooning) funded by the Ministry of Land, Infrastructure and Transport (MOLIT Korea).  
The authors declare no conflict of interest.