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Self-Driving Cars With Convolutional Neural Networks (CNN) - neptune.ai

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Humanity has been waiting for self-driving cars for several decades. Thanks to the extremely fast evolution of technology, this idea recently went from "possible" to "commercially available in a Tesla". Deep learning is one of the main technologies that enabled self-driving. It's a versatile tool that can solve almost any problem – it can be used in physics, for example, the proton-proton collision in the Large Hadron Collider, just as well as in Google Lens to classify pictures. Deep learning is a technology that can help solve almost any type of science or engineering problem. CNN is the primary algorithm that these systems use to recognize and classify different parts of the road, and to make appropriate decisions. Along the way, we'll see how Tesla, Waymo, and Nvidia use CNN algorithms to make their cars driverless or autonomous. The first self-driving car was invented in 1989, it was the Automatic Land Vehicle in Neural Network (ALVINN). It used neural networks to detect lines, segment the environment, navigate itself, and drive. It worked well, but it was limited by slow processing powers and insufficient data.


Powering Data-Driven Autonomy at Scale with Camera Data

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At Woven Planet Level 5, we're using machine learning (ML) to build an autonomous driving system that improves as it observes more human driving. This is based on our Autonomy 2.0 approach, which leverages machine learning and data to solve the complex task of driving safely. This is unlike traditional systems, where engineers hand-design rules for every possible driving event. Last year, we took a critical step in delivering on Autonomy 2.0 by using an ML model to power our motion planner, the core decision-making module of our self-driving system. We saw the ML Planner's performance improve as we trained it on more human driving data.


How Zoox vehicles "find themselves" in an ever-changing world

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For a human to drive successfully around an urban environment, they must be able to trust their eyes and other senses, know where they are, understand the permissible ways to move their vehicle safely, and of course know how to reach their destination. Building these abilities, and so many more, into an autonomous electric vehicle designed to transport customers smoothly and safely around densely populated cities takes an astonishing amount of technological innovation. Since its founding in 2014, Zoox has been developing autonomous ride-hailing vehicles, and the systems that support them, from the ground up. The company, which is based in Foster City, California, became an independent subsidiary of Amazon in 2020. The Zoox purpose-built robot is an autonomous, pod-like electric vehicle that can carry four passengers in comfort.


In 2022, the most important trends in AI and Machine Learning will alter the timeline

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With the Covid-19 outbreak, companies from all walks of life are leveraging advanced technology to revolutionize the way we work and live. Over the past few years, technology has undoubtedly become an important feature and guideline in times of crisis. Artificial intelligence, machine learning and other related technologies have the potential to transform traditional business models from the most basic to the simplest, efficient and inexpensive. The "smart" component of smart digital solutions refers to artificial intelligence and machine learning. These two fundamental elements are called the "brains" of intelligent machines, which are used to provide efficient and efficient business solutions.


Council Post: Three Ways AI Is Impacting The Automobile Industry

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Wendy Gonzalez is the CEO of Sama, the provider of accurate data for ambitious AI. Autonomous cars are as intrinsic to visions of the future as holograms and space travel. Since the birth of science fiction, the automobile has been seen as the final frontier of technological innovation. However, when we look around at our cities today, cars can often seem stuck in the past. The reality is that the vision for the automotive industry has far exceeded the pace of its progress.


Hyundai chooses IonQ's quantum tech to improve its vehicles' object recognition capabilities

ZDNet

IonQ announced an expansion of its relationship with automaker Hyundai that will see it applying its quantum computing technology to the task of allowing Hyundai vehicles to better recognize real-world objects. This new collaboration builds on an existing relationship that began earlier this year which saw IonQ's quantum tech being used to improve the efficiency and cost-effectiveness of Hyundai's electric vehicle (EV) batteries. The companies hope that the application of quantum machine learning to in-vehicle computer vision systems will allow both automated and human-controlled vehicles to better recognize objects on the road and beside it for safety and autonomous driving purposes. The duo claims they have already classified 43 different types of road signs for recognition using quantum machine learning tech. More: What is machine learning?


Agent-Based Modeling for Predicting Pedestrian Trajectories Around an Autonomous Vehicle

Journal of Artificial Intelligence Research

This paper addresses modeling and simulating pedestrian trajectories when interacting with an autonomous vehicle in a shared space. Most pedestrian–vehicle interaction models are not suitable for predicting individual trajectories. Data-driven models yield accurate predictions but lack generalizability to new scenarios, usually do not run in real time and produce results that are poorly explainable. Current expert models do not deal with the diversity of possible pedestrian interactions with the vehicle in a shared space and lack microscopic validation. We propose an expert pedestrian model that combines the social force model and a new decision model for anticipating pedestrian–vehicle interactions. The proposed model integrates different observed pedestrian behaviors, as well as the behaviors of the social groups of pedestrians, in diverse interaction scenarios with a car. We calibrate the model by fitting the parameters values on a training set. We validate the model and evaluate its predictive potential through qualitative and quantitative comparisons with ground truth trajectories. The proposed model reproduces observed behaviors that have not been replicated by the social force model and outperforms the social force model at predicting pedestrian behavior around the vehicle on the used dataset. The model generates explainable and real-time trajectory predictions. Additional evaluation on a new dataset shows that the model generalizes well to new scenarios and can be applied to an autonomous vehicle embedded prediction.


Global Automotive Artificial Intelligence (AI) Market is Forecast to Grow to US$7,676.92 Million by 2028, with a CAGR of 31.30% in the 2022-2028 period

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Artificial intelligence (AI) is a cutting-edge computer science technology. It shares similarities with human intelligence in terms of language comprehension, reasoning, learning, problem solving. In the development and revision of technology, market manufacturers face enormous intellectual challenges during the forecast period. Furthermore, the expansion of the automotive industry is expected to drive the Automotive Artificial Intelligence Market during the forecast period. The automotive industry has recognized the potential of artificial intelligence and is one of the major industries that employs AI to augment and mimic human action which is the major factor driving the growth of Automotive Artificial Intelligence Market during the forecast period.


9 Lessons from the Tesla AI Team

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. While OpenAI is famous for its success in NLP and DeepMind is well known for RL and decision making, Tesla is definitely one of the most impactful companies in computer vision.


Deep Learning First: Drive.ai's Path to Autonomous Driving

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Last month, IEEE Spectrum went out to California to take a ride in one of Drive.ai's It's only been about a year since Drive.ai "This is in contrast to a traditional robotics approach," says Sameep Tandon, one of Drive.ai's "A lot of companies are just using deep learning for this component or that component, while we view it more holistically." Often, deep learning is used in perception, since there's so much variability inherent in how robots see the world.