End to End Learning for Self-Driving Cars

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We trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands. This end-to-end approach proved surprisingly powerful. With minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways. It also operates in areas with unclear visual guidance such as in parking lots and on unpaved roads. The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal. We never explicitly trained it to detect, for example, the outline of roads. Compared to explicit decomposition of the problem, such as lane marking detection, path planning, and control, our end-to-end system optimizes all processing steps simultaneously. We argue that this will eventually lead to better performance and smaller systems. Better performance will result because the internal components self-optimize to maximize overall system performance, instead of optimizing human-selected intermediate criteria, e.g., lane detection. Such criteria understandably are selected for ease of human interpretation which doesn't automatically guarantee maximum system performance. Smaller networks are possible because the system learns to solve the problem with the minimal number of processing steps. We used an NVIDIA DevBox and Torch 7 for training and an NVIDIA DRIVE(TM) PX self-driving car computer also running Torch 7 for determining where to drive. The system operates at 30 frames per second (FPS).

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Autopilot-Steering-Wheel-Simulation

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Deep Learning with Keras - Run the simulator to drive the car itself

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SDC Nanodegree - Behavioral Clonning

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A TensorFlow implementation of this Nvidia paper: https://arxiv.org/pdf/1604.07316.pdf with some changes

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Teach a car to drive in a simulator using the NVIDIA End to End Learning for Self-Driving Cars model.

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Machine learning for a driver less vehicle

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Deep Learning to Clone Driving Behavior

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Udacity Behavioral Cloning Project 3

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carndp4writep

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A toolkit for controlling Euro Truck Simulator 2 with python to develop self-driving algorithms.

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This is an implementation of End to End Learning for Self-Driving Cars model developed by NVIDIA.

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Autopilot-Steering-Wheel-Simulation

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A Keras implementation of this Nvidia paper: https://arxiv.org/pdf/1604.07316.pdf with some changes

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A TensorFlow implementation of this Nvidia paper: https://arxiv.org/pdf/1604.07316.pdf with some changes

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Third Project of the Udacity Self-Driving Car Nanodegree Program

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Udacity self-driving car nanodegree project 3 : Behavioral cloning

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Behavioral-Cloning-Using-Nvidia-Model

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In this project, we use deep learning to imitate human driving in a simulator. In particular, we utilize Keras libraries to build a convolutional neural network that predicts steering angle response in the simulator.

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An implementation of end-to-end learning for self-driving cars of Nvidia paper (Bojarski et al.2016) using keras

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All the files needed for the Terrapin Hackers Hacktorial on Behavioral Cloning for Self Driving Cars.

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Udacity CarND Behaviour Cloning Project 3

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End-to-End learning for self driving car

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Final Year Project on Self Driving Car using Udacity's Self Driving Car Simulator

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A very basic self-driving car model to train road recognition and steering.

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ENGN8536 Final Project "END TO END LEARNING FOR SELF DRIVING PLANE IN GTA V

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End-to-End learning to train a simulated car keep on the track without crash

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Keras implementation of End to End Learning for Self-Driving Cars by Nvidia.

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This implementation helps in predicting steering angles for a self driving car.

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Using a convolution neural network in Keras that predicts steering angles from images for autonomous driving

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Using CNNs to predict steering angles

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end to end learning for self-driving

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Implementation of behavioral cloning

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Udacity nanodegree Project 3 - Behavior cloning

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end to end learning for self driving cars based on CNN

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github.com: /heechul/picar

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End-to-end deep learning based autonomous RC car using Raspberry Pi 3.

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Autonomous driving using behavioral cloning.

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self-driving car nanodegree project 3 behavioral cloning

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Build a self driving car using deep reinforcement learning.

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A End to End CNN Model which predicts the steering wheel angle based on the video/image

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Keras Implementation of End to End Learning for Self-Driving Cars by (Baris Kayalibay, Grady Jensen, Patrick van der Smagt)

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Implementation of an end to end deep neural network in keras which predicts steering angles based on raw image data to navigate a car

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Udacity Self-Driving Car Engineer Class Project, due on Jan 30th, 2017

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Behavioral Cloning Project

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Drive a car using deep learning

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Udacity Self-Driving Car Engineer Nanodegree Project Behavioral Cloning

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In this project a end-to-end-solution for autonomous lane keeping is developed using imitation learning. The goal is to develop a robust lane keeping system for a model scale car (scale 1:8) for indoor szenarios (e.g. driving szenarios of the Carolo Cup).

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Implementation of the CNN from End to End Learning for Self-Driving Cars on a Nvidia Jetson TX1 using Tensorflow and ROS

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Steering wheel Rotation movement using Computer vision

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Python Keras Based Behavior Cloning(Car Control) System

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github.com: /marshq/europilot

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A toolkit for controlling Euro Truck Simulator 2 with python to develop self-driving algorithms.

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Train a deep neural network to drive a car like you

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Human-level control through deep reinforcement learning

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End to end learning for self-driving car.

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Behavioral Cloning for Self Driving Cars powered by convolutional neural networks

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Steering, acceleration and brake self-driving car model inspired from Sully Chen & Udacity Dataset

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An implementation of "End-to-end learning for self-driving cars" paper by NVIDIA team

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This Project includes Udacity simulator and code for self drive.

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Machine Learning for RC Cars

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Udacity Term 1 - Project 3 drives a car around a simulated track. This program uses a Convolutional Neural Network which was taught how to drive. This was accomplished by collecting data (image and steering angle). This data was used to train the neural network, and to drive a vehicle in a simulator.

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Deep Learning Project to Teach a Car to Drive Autonomously Using Only Camera Images.

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In a new automotive application, we have used convolutional neural networks (CNNs) to map the raw pixels from a front-facing camera to the steering commands for a self-driving car. This powerful end-to-end approach means that with minimum training data from humans, the system learns to steer, with or without lane markings, on both local roads and highways. The system can also operate in areas with unclear visual guidance such as parking lots or unpaved roads. In this Project i am going to implement Nvidia End-to-End Deep Learning for Self-Driving Cars" Network Architecture(https://arxiv.org/pdf/1604.07316.pdf) and around 45,000 Data-set(https://github.com/SullyChen/driving-datasets)

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Behavioural Cloning project for Udacity Self Driving Car NanoDegree

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Keras/Tensorflow Implementation of End-to-End Learning for self-driving car (Nvidia). Spatial factorization is additionally implemented to reduce network size.

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end 2 end

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Repo for Predicting Steering Wheel

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A self driving car model for humans.

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Implement Nvidia's paper on self driving cars using tensorflow.

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The objective of this project is to implement an end to end learning system which learns from manually driven scenarios how to drive safely around a circuit in a simulator. The CNN inputs are raw images and the output is the predicted steering angle.

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Deep learning to train an autonomous vehicle to mimic human driving

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Behavorial Cloning for Car Simulation using a Neural Network in Keras

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Behavior Cloning - Self Driving Car Project

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An autopilot for the Udacity SDCND simulator based on deep neural networks

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Self driving car for GTA5 using a CNN

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Behavioral cloning: end-to-end learning for self-driving cars.

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