Congreso Campus FIT 2020. 24-26 junio 2020, onlineThe introduction of Autonomous Vehicles (AVs) in a realistic urban environment is an
ambitious objective. AV validation on real scenarios involving actual objects such as cars or
pedestrians in a wide range of traffic cases would escalate the cost and could generate
hazardous situations. Consequently, autonomous driving simulators are quickly evolving to
cover the gap to achieve a fully autonomous driving architecture validation. Most used 3D
simulators in self-driving cars field are V-REP (Rohmer, E., 2013) and Gazebo (KOENIG,
N. and HOWARD, A., 2004), due to an easy integration with ROS (QUIGLEY, 2009)
platform to increase the interoperability with other systems. Those simulators provide
accurate motion information (more appropriate for easier scenes like robotic arms) but not a
realistic appearance and not allowing real-time systems, not being able to recreate complex
traffic scenes. CARLA (DOSOVITSKIY, A., 2017) open-source AV simulator is designed
to be able to train and validate control and perception algorithms in complex traffic scenarios
with hyper-realistic environments. CARLA simulator allows to easily modify on-board
sensors such as cameras or LiDAR, weather conditions and also the traffic scene to perform
specific traffic cases. In Summer 2019, CARLA launched its driving challenge to allow
everyone to test their own control techniques under the same traffic scenarios, scoring its
performance regarding traffic rules. In this paper, the Robesafe researching group approach
will be explained, detailing vehicle motion control and object detection adapted from Smart
Elderly Car (GÓMEZ-HUÉLAMO, C., 2019) that lead the group to reach the 4th place in
Track 3 challenge, where HD Map, Waypoints and environmental sensors data (LiDAR,
RGB cameras and GPS) were provided.Ministerio de Ciencia, Innovación y UniversidadesComunidad de Madri
