How to build and validate a safe and reliable Autonomous Driving stack? A ROS based software modular architecture baseline

The implementation of Autonomous Driving stacks (ADS) is one of the most challenging engineering tasks of our era. Autonomous Vehicles (AVs) are expected to be driven in highly dynamic environments with a reliability greater than human beings and full autonomy. Furthermore, one of the most important topics is the way to democratize and accelerate the development and research of holistic validation to ensure the robustness of the vehicle. In this paper we present a powerful ROS (Robot Operating System) based modular ADS that achieves state-of-the-art results in challenging scenarios based on the CARLA (Car Learning to Act) simulator, outperforming several strong baselines in a novel evaluation setting which involves non-trivial traffic scenarios and adverse environmental conditions (Qualitative results). Our proposal ranks in second position in the CARLA Autonomous Driving Leaderboard (Map Track) and gets the best score considering modular pipelines, as a preliminary stage before implementing it in our real-world autonomous electric car. To encourage the use research in holistic development and testing, our code is publicly available at https://github.com/RobeSafe-UAH/CARLA_Leaderboard.