End-to-End Deep Learning approach for Autonomous Lane Navigation. Imitation Learning implemented using Duckie Town Simulator. The architecture is based on the proposed NVIDIA’s DAVE-2.
Computer Vision and Camera Calibration for Self Driving Cars. Introduction to concepts like Camera Calibration, Perspective Transform and Distortion for Self Driving Cars
Introduction to Convolutional Neural Networks for Self Driving Cars. Introductory concepts in the field of Image Recognition using Convolutional Neural Networks
Foundational concepts in the fields of Machine Learning and Deep Neural Networks. Now we will learn how to use one of the most exciting tools and self-driving car development, deep neural networks. A deep neural network is just a term that describes a big multi-layer neural network.
Introduction to Neural Networks For Self Driving Cars. Foundational concepts in the fields of Machine Learning, Deep Neural Networks and Self Driving Cars
Foundational Concepts in the field of Deep Learning and Machine Learning. We’ll focus on TensorFlow because if one becomes a machine learning expert, these are the tools that people in the trade use everyday.
In this medium article, I’m going to explain the basics concepts behind Keras, Transfer Learning and Multilayer Convolutional Neural Network. I’ll be introducing an interface that sits on top of TensorFlow, and allows us to draw on the power of TensorFlow with far more concise code. Introduction to Keras and the use of Transfer Learning in the development of Deep Learning architectures. Introduction to Keras & Transfer Learning for Self Driving Cars
Flit towards AVs — how fleet management technology can help? I will focus on three key components already covered within fleet management, and explain how they contribute to the future of AVs.
Imitating the nematode’s nervous system to process information efficiently, this new intelligent system is more robust, more interpretable, and faster to train than current deep neural network architectures with millions of parameters.
In this article, we will discuss PointPillars by Gregory P. Meyer et. al. Compared to the other works we discuss in this area, PointPillars is one of the fastest inference models with great accuracy on the publicly available self-driving cars dataset.
ECCV 2020 digest. The most interesting self-driving research from experts. Two weeks ago thousands of computer vision researchers gathered virtually at the European Conference on Computer Vision (ECCV) to present their latest results.
Tutorial to implement a Pillar Based Object Detection Deep Neural Net on Amazon Sage Maker. This can be generalized to any cloud instance or even local environments.
Classification of Traffic Signs Using Deep Learning. This article will explain all the steps taken to design a Deep Learning model to do that.
Today we are going to talk about object detection, a branch of computer vision, a field that is widely used in self-driving cars to detect pedestrians or signs etc...
Traffic Light Detection for a self-driving car — a step-by-step guide how to apply Tensorflow Object Detection API
Traffic Light Detection for a Self-driving Car — a step-by-step guide on how to apply Tensorflow Object Detection API. Object Detection by Tensorflow 1.x
Uber’s ATG has outdone its competitors in self-driving vehicles technology.
And which technology is ideal for self-driving cars. Almost every single company working on self-driving cars right now uses LIDAR. Uber, Waymo, and Toyota all use it, but not Tesla. I want to go over what the two competing technologies have to offer and what we should expect from self-driving cars in the future.
During the Coronavirus pandemic, people’s attitudes towards public transportation and carsharing have changed. They are not safe enough. Precisely because of that, most users are now finding comfort in owning a private vehicle.
Self-driving cars are the future, but many argue that the driverless future is most times oversold for what it is, as so much optimism hides the many obstacles on the road to the self-driving future, many of which include;