Filipe Wall Mutz

  • Citations Per Year
Learn More
In this work, we present an end-to-end framework for precise large-scale mapping with applications in autonomous driving. In special, the problem of mapping complex environments, with features changing from tree-lined streets to urban areas with dense traffic, is studied. The robotic car is equipped with a odometry sensor, a 3D LiDAR Velodyne HDL-32E, a(More)
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(More)
In this paper, we propose a simulator for robotic cars based on two time-delay neural networks. These networks are intended to simulate the mechanisms that govern how a set of effort commands changes the car's velocity and the direction it is moving. The first neural network receives as input a temporal sequence of current and previous throttle and brake(More)
We propose a light-weight yet accurate localization system for autonomous cars that operate in large-scale and complex urban environments. It provides appropriate localization accuracy and processing time at high frequency suitable for fast control actions, besides low power consumption desirable for limited energy availability in commercial cars. The(More)
We present a biologically inspired approach to traffic sign detection based on Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN). VG-RAM WNN are effective machine learning tools that offer simple implementation and fast training and test. Our VG-RAM WNN architecture models the saccadic eye movement system and the(More)
We propose a Neural Based Model Predictive Control (N-MPC) approach to tackle delays in the steering plant of autonomous cars. We examined the N-MPC approach as an alternative for the implementation of the Intelligent and Autonomous Robotic Automobile (IARA) steering control subsystem. For that, we compared the standard solution, based on the Proportional(More)
Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN) is an effective machine learning technique that offers simple implementation and fast training and test. We examined the performance of VG-RAM WNN on binocular dense stereo matching using the Middlebury Stereo Datasets. Our experimental results showed that, even without(More)
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(More)
We propose a Virtual Generalizing Random Access Memory (VG-RAM) Weightless Neural Network (WNN) Computer (V'Ger Computer for short). VG-RAM WNNs are very effective pattern recognition tools, offering fast training (one shot training) and competitive recognition performance, if compared with other current techniques. The V'Ger Computer architecture was(More)
  • 1