alexispap51

Deep-Learning-for-Autonomous-Driving

The purpose of this project is the development of an End-to-End learning model in order to predict the steering angle of an autonomous car. The proposed method uses monocular vision in order to acomplish the prediction task. Specifically, a CNN followed by LSTM units, is trained in order to manage both spatial and temporal information of the image sequence. In addition, a fusion with a second CNN that uses past prediction as inputs, is proposed, in order to improve the temporal information available. Both of the architectures were trained and tested on human driving data, provided by Udacity Challenge 2.

gazebo_models

Model database

gtsam

GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.

topological_navigation

alexispap51/Deep-Learning-for-Autonomous-Driving

The purpose of this project is the development of an End-to-End learning model in order to predict the steering angle of an autonomous car. The proposed method uses monocular vision in order to acomplish the prediction task. Specifically, a CNN followed by LSTM units, is trained in order to manage both spatial and temporal information of the image sequence. In addition, a fusion with a second CNN that uses past prediction as inputs, is proposed, in order to improve the temporal information available. Both of the architectures were trained and tested on human driving data, provided by Udacity Challenge 2.

alexispap51/gazebo_models

Model database

alexispap51/gtsam

GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.

alexispap51/topological_navigation