We present the Model-Predictive Motion Planner (MPMP) of the Intelligent Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses a path planner to compute a path from its current position to the desired destination. Using this path, the current position, a goal in the path and a map, IARA's MPMP is able to compute smooth trajectories from its current position to the goal in less than 50 ms. MPMP computes the poses of these trajectories so that they follow the path closely and, at the same time, are at a safe distance of occasional obstacles. Our experiments have shown that MPMP is able to compute trajectories that precisely follow a path produced by a Human driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of up to 32.4 km/h (9 m/s).

In this work, we present a software architecture to solve, at some level, the follow the leader problem. This problem consists of an autonomous vehicle trying to track and follow a leader vehicle. To track the leader position in consecutive camera images, we employed the Generic Object Tracking Using Regression Networks (GOTURN). GOTURN is a pre-trained Deep Neural Network capable of tracking generic objects, without application-specific training or fine-tuning. The proposed software architecture was evaluated using a real autonomous vehicle, in four stretches of a University ring road. In all experiments, the autonomous vehicle was able to follow the leader's path with maximum root mean square error of 0.28m.