  The objective of the first CARLA autonomous driving challenge was to deploy
autonomous driving systems to lead with complex traffic scenarios where all
participants faced the same challenging traffic situations. According to the
organizers, this competition emerges as a way to democratize and to accelerate
the research and development of autonomous vehicles around the world using the
CARLA simulator contributing to the development of the autonomous vehicle area.
Therefore, this paper presents the architecture design for the navigation of an
autonomous vehicle in a simulated urban environment that attempts to commit the
least number of traffic infractions, which used as the baseline the original
architecture of the platform for autonomous navigation CaRINA 2. Our agent
traveled in simulated scenarios for several hours, demonstrating his
capabilities, winning three out of the four tracks of the challenge, and being
ranked second in the remaining track.
  Our architecture was made towards meeting the requirements of CARLA
Autonomous Driving Challenge and has components for obstacle detection using 3D
point clouds, traffic signs detection and classification which employs
Convolutional Neural Networks (CNN) and depth information, risk assessment with
collision detection using short-term motion prediction, decision-making with
Markov Decision Process (MDP), and control using Model Predictive Control
(MPC).
