A self-driving automobile is one that can sense its surroundings and operate without the need for human intervention.For decades, engineers have experimented with self-driving automobile prototypes. The concept is simple: equip a car with cameras that can track all of the things in its immediate vicinity and have the car respond if it is going to drive into one.
So if you are interested to know the backend coding of a self-driving car, do research, or want to develop your skill in this field, this article is for you.
Here we are going to talk about some backend technologies of self-driving cars and share famous Github repositories to give a clear idea about the backend coding of self-driving cars.
The ndrplz/self-driving-car repository documents the source code of all the projects of Udacity Self-Driving Car tutorial series. Which shows the techniques that power self-driving cars across the full stack of a vehicle’s autonomous capabilities. The project mainly discussed computer vision, deep-learning, pid-control, and many other important topics about self-driving cars. Mainly the projects are developed in C++ and python.
The repository of SandeepAswathnarayana/Udacity-SDCND-Programming-a-Real-Self-Driving-Car explains how to program a Real Self-Driving Car in Python to navigate the car around the track while adhering to the laws of the road. The project's major focus is on localization, mapping, and path-planning. Python is the project's major programming language.
This Github repository of darienmt/CarND-TrafficSignClassifier-P2 is a project of recognizing traffic signs. The goal of the project is to teach a Convolutional Neural Network how to identify traffic signals. Computer-vision, a neural network to detect traffic lights is discussed here. It will give a clear understanding of neural networks, also you can learn traffic signs recognition algorithms from this project. It will give you a strong start in this field of autonomous cars. This project contains the Ipython notebook and the writeup template.
Check out this Github repository of srinu6/Stereo-3D-Object-Detection-for-Autonomous-Driving for 3D Machine Learning for autonomous driving. It shows a method of object detection for autonomous driving. It is implemented using Stereo R-CNN and Stereo R-CNN is an extended implementation of Faster R-CNN. From this project, you can learn about convolutional-network, stereo-rcnn, stereo-3d. The project is developed in python.
ser94mor/self-driving-car-using-ros is an object detecting project of autonomous cars. Implementing a traffic light detector/classifier that identifies the color of the next incoming traffic light and publishes it to the /waypoint updater node so it can prepare the automobile to speed up or slow down accordingly is a significant portion of the project. This project will teach you about computer vision and ROS in Python.
Check out this Github repository of OanaGaskey/Self-Driving-Car-Capstone. The project's objective is to completely implement the key modules of an autonomous vehicle using ROS: perception, planning, and control. The project is written in the ROS programming language. This project makes use of rospy, a pure Python ROS client package that allows Python programmers to interact with ROS Topics, Services, and Parameters.
KonstantinosBarmpas/Traffic-Light-Classifier built a project to design two Traffic Light Classifier models. It shows the training method of the models, generates results and comments on the results. From this project, you can learn traffic light detection and classification algorithms and train your model according to them. Also, you will learn Tensorflow object detection API, SSD_mobilnet along with python is used in this project.
The godloveliang/Programming-a-Real-Self-Driving-Car repository contains all you need to know about programming a real self-driving car. ROS nodes are built for this project to provide fundamental autonomous vehicle system functions, such as traffic light detection, control, and waypoint following. The idea is based on Karla, a genuine self-driving vehicle. This project will teach you all of the backend technologies needed to create a self-driving automobile.
This upul/Traffic-Signs project used deep neural networks and convolutional neural networks to classify traffic signs. Then a model is trained so it can decode traffic signs from natural images deep-learning, TensorFlow, classification are the main topics of the project.
This repository of fazilaltinel/SelfDrivingCarTrafficLight includes a basic TensorFlow implementation of the Udacity annotated self-driving dataset's traffic light detection and classification job. The project's major subjects are tensorflow and classification. So after completing it you will get a clear idea about tensorflow, classification algorithms in self-driving cars.
The team of justinlee007/CarND-Capstone has created several ROS nodes to implement core functionality of an autonomous vehicle like lane detection, traffic light detection, traffic sign detection, object and free space detection It used Histogram of Oriented Gradients (HOG) techniques in image manipulation. It will boost your skill to another level in this field.
The repository microsoft/AirSim is an Unreal Engine-based simulator for drones, automobiles, and other vehicles. It's free, cross-platform, and can simulate software-in-the-loop with a variety of flight controllers. This project will teach you about deep reinforcement learning, deep learning, control systems, and cross-platform for autonomous cars.
The repository carla-simulator/carla is an open-source simulator for research on self-driving cars. CARLA was built from the bottom up to aid in the creation, training, and certification of self-driving vehicles. CARLA delivers open digital assets in addition to open-source code and protocols. This project will teach you imitation learning, unreal-engine-4, and Carla-simulator.
The repository Autoware-AI/autoware.ai is the world's first "all-in-one" self-driving car open-source software. Autoware's capabilities are most suited for metropolitan areas, although it can also cover highways, freeways, regions, and geofenced locations. This project will teach you about ROS, calibration, 3d-mapping, and autoware. It will substantially improve your AI abilities.
The repository cfzd/Ultra-Fast-Lane-Detection is a project on ultra-fast lane detection for autonomous cars. This is a research paper-based project where the researchers implemented a self-driving car that can detect an ultra-fast lane in its destination The paper has been accepted by ECCV2020. PyTorch is implemented in this project. Python is the primary language of the project. You can also learn CNN, lane-finding algorithms from this project.
This repository of zhm-real/MotionPlanning included some of the most popular motion planners seen in self-driving cars. It employs two models: a simple automobile and a car towing a trailer. Motion-planning algorithms, path-planning algorithms, Stanley-controller, hybrid-a-star, and wheel-feedback are all covered here. The project is being developed in Python.
thibo73800/metacar is a browser-based project that creates a 2D reinforcement learning environment for autonomous cars. By solving interesting puzzles, the initiative seeks to make reinforcement learning more accessible to everyone. The project will teach you reinforcement learning, Pixi js, and TensorFlow js. This project was created using Typescript.
dctian/DeepPiCar is a DeepPiCar DashCam project for Traffic Sign and People Detection. This is the sixth section of a six-part article. Here's a step-by-step guide on putting it all together. This project will teach you raspberry-pi, OpenCV, computer vision, deep learning, and TensorFlow. You will be able to make your automobile detect and follow lanes, identify and respond to traffic signs and people on the road after finishing the project.
sigmaai/self-driving-golf-cart is an open-source self-driving development platform that's geared for quick prototyping, deep learning, and robotics research. Currently, the system is powered by a modified electric golf cart. You will have a thorough understanding of microcontrollers, navigation, path planning, ROS, convolutional neural networks, object identification, behavioral cloning, semantic segmentation, and Carla after finishing the project. It will help you take your self-driving vehicle learning to the next level.
Pylot is a self-driving car platform that allows developers and testers to create and test autonomous vehicle components. An implementation of the autonomous vehicle using pylot may be found in the erdos-project/pylot repository. Vehicle control, path prediction, and path planning algorithms will all be covered. This project will also teach you how to detect traffic lights, simulate an automobile, and track obstacles.
In this repository of Habrador/Self-driving-vehicle, a path-planning algorithm was built. You'll be able to discover a drivable shortest path to the objective using that approach. The Hybrid A Star search algorithm is something you learn. You'll be able to find a drivable path to the target using that algorithm. The project is written in the C# programming language.
In this repository enginBozkurt/LidarObstacleDetection a project to filter, segment, and cluster actual point cloud data to detect impediments in a driving environment is developed. The goal of this project is to provide a pipeline for turning raw LIDAR sensor readings into trackable objects. From the project, you will learn about lidar measurements, point-cloud-library, lidar-point-cloud, lidar-object-tracking. C++ is used to implement the project.
This repository of tj27-vkr/RCNN-Vehicle-Tracking-Lane-Detection focuses on Mask R-CNN-based Vehicle Detection and Computer Vision-based Lane Detection. Keras and TensorFlow were used to implement the Mask R-CNN. The model uses segmentation masks with pre-learned weights trained on the COCO dataset to recognize cars in the picture frame, and the Sobel filter is used to detect lanes.
This repository of sidroopdaska/SelfDrivingRCCar documents the development and construction of a self-driving RC vehicle. The project uses Neural Networks and OpenCV to create a scaled-down version of a self-driving system. The system consists of a Raspberry Pi with inputs from a camera and an ultrasonic sensor.
This repository of dineshresearch/Novel-Deep-Learning-Model-for-Traffic-Sign-Detection-Using-Capsule-Networks contains a capsule network that performs exceptionally well on the German traffic sign dataset. On the German Traffic Sign Recognition Benchmark dataset, the capsule network obtained a state-of-the-art accuracy of 97.6%. This project will teach you about traffic-sign classification, traffic-sign-recognition, traffic-sign-detection, and capsule-network. This project was created using Python.
So, if you're a researcher or intending to do research or a final year student who wants to do a project on self-driving cars, you now have some options and guidelines to choose one. These articles will help you improve your abilities and provide recommendations for your research or project.
If you are interested to learn more about self-driving cars you can visit our self-driving tutorial section.
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