Building a Pipeline for Efficient Production of Synthetic Datasets for Improving RL in Automated Driving

Online deep reinforcement learning training poses challenges due to its length and instability, despite the development of learning algorithms targeted to overcome these issues. Offline learning has emerged as a potential solution, but it reintroduces the issue of dataset production, which is resource-consuming and challenging even in simulation environments. This paper investigates efficient dataset creation for offline learning in the context of automated driving. Our proposed solution is a pipeline based on the CARLA simulator, which offers a wide variety in terms of car models, weather conditions, and environments. The pipeline aims to produce high-quality datasets for pre-training, training, and fine-tuning models, targeting improved training speed and reduced divergence. By leveraging CARLA's level of realism, we address the resource-intensive nature of dataset production, providing researchers and car manufacturers with a valuable tool for advancing the development of robust automated driving systems.