Improving benchmarks for autonomous vehicles testing using synthetically generated images
This provides an incremental solution for improving autonomous vehicle development in data-scarce regions.
The paper tackles the problem of autonomous vehicles struggling to recognize traffic signs in countries with limited training data by proposing a method to update models using small local datasets, achieving about a 10% quality improvement.
Nowadays autonomous technologies are a very heavily explored area and particularly computer vision as the main component of vehicle perception. The quality of the whole vision system based on neural networks relies on the dataset it was trained on. It is extremely difficult to find traffic sign datasets from most of the counties of the world. Meaning autonomous vehicle from the USA will not be able to drive though Lithuania recognizing all road signs on the way. In this paper, we propose a solution on how to update model using a small dataset from the country vehicle will be used in. It is important to mention that is not panacea, rather small upgrade which can boost autonomous car development in countries with limited data access. We achieved about 10 percent quality raise and expect even better results during future experiments.