Autonomous Vehicles: Overviews


What Is Tesla Autopilot? Answers For FAQ CleanTechnica

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Seemingly, one of the most controversial things about Tesla cars is its Autopilot feature, a driver-assist feature that helps drivers navigate and pilot their vehicle. Oddly, while news of exciting Autopilot features comes out regularly, general information about exactly what Autopilot is, what the options are, and what it can and cannot do seem to be few and far between. I have tried to collect and answer the biggest questions about Autopilot below to help prospective buyers know what the system is and is not, as well as to inform journalists about the system in case they find themselves trying to cover a news story regarding the system. When the next questionable news story comes out, please feel free to link this article for anyone wondering about the system. Please note that all of the below information refers to Tesla vehicles containing Autopilot 2.0 hardware or higher in them (vehicles built since October of 2016). Although, the majority of the information will apply to all Tesla vehicles that are Autopilot enabled.


A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

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Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed.


Decision making in dynamic and interactive environments based on cognitive hierarchy theory: Formulation, solution, and application to autonomous driving

arXiv.org Artificial Intelligence

Abstract-- In this paper, we describe a framework for autonomous decision making in a dynamic and interactive environment based on cognitive hierarchy theory. We model the in - teractions between the ego agent and its operating environm ent as a two-player dynamic game, and integrate cognitive behav - ioral models, Bayesian inference, and receding-horizon op timal control to define a dynamically-evolving decision strategy for the ego agent. Simulation examples representing autonomou s vehicle control in three traffic scenarios where the autonom ous ego vehicle interacts with a human-driven vehicle are repor ted. Autonomous systems are becoming more capable, better accepted, and more commonplace. Many autonomous systems, including collaborative robots [1] and self-driv ing cars [2], operate in dynamic and interactive environments.


A Guide to Using AI for Marketing

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As technology increases, so do the tools that we as marketers have at our disposal. One of the more recent developments, artificial intelligence (AI), is no exception. While most just hear AI and think of self-driving cars or sentient robots, this is an area of science that we can use to help us be more effective as marketers. AI is the method of using machine learning to mimic human intelligence, patterns and tendencies. Computers use algorithms and historical data to determine how to respond to certain actions.


A Guide to Using AI for Marketing

#artificialintelligence

As technology increases, so do the tools that we as marketers have at our disposal. One of the more recent developments, artificial intelligence (AI), is no exception. While most just hear AI and think of self-driving cars or sentient robots, this is an area of science that we can use to help us be more effective as marketers. AI is the method of using machine learning to mimic human intelligence, patterns and tendencies. Computers use algorithms and historical data to determine how to respond to certain actions.


Cooperative Automated Vehicles: a Review of Opportunities and Challenges in Socially Intelligent Vehicles Beyond Networking

arXiv.org Artificial Intelligence

The connected automated vehicle has been often touted as a technology that will become pervasive in society in the near future. One can view an automated vehicle as having Artificial Intelligence (AI) capabilities, being able to self-drive, sense its surroundings, recognise objects in its vicinity, and perform reasoning and decision-making. Rather than being stand alone, we examine the need for automated vehicles to cooperate and interact within their socio-cyber-physical environments, including the problems cooperation will solve, but also the issues and challenges. We review current work in cooperation for automated vehicles, based on selected examples from the literature. We conclude noting the need for the ability to behave cooperatively as a form of social-AI capability for automated vehicles, beyond sensing the immediate environment and beyond the underlying networking technology.


Seeing rough road ahead, Ford sheds 7,000 white-collar jobs

The Japan Times

DETROIT - Ford revealed details of its long-awaited restructuring plan Monday as it prepared for a future of electric and autonomous vehicles by parting ways with 7,000 white-collar workers worldwide, about 10 percent of its global salaried workforce. The major revamp, which had been underway since last year, will save about $600 million per year by eliminating bureaucracy and increasing the number of workers reporting to each manager. In the U.S. about 2,300 jobs will be cut through buyouts and layoffs. About 1,500 have left voluntarily or with buyouts, while another 300 have already been laid off. About 500 workers will be let go starting this week, largely in and around the company's headquarters in Dearborn, Michigan, just outside Detroit.


Can a Robot Become a Movie Director? Learning Artistic Principles for Aerial Cinematography

arXiv.org Artificial Intelligence

Aerial filming is becoming more and more popular thanks to the recent advances in drone technology. It invites many intriguing, unsolved problems at the intersection of aesthetical and scientific challenges. In this work, we propose an intelligent agent which supervises motion planning of a filming drone based on aesthetical values of video shots using deep reinforcement learning. Unlike the current state-of-the-art approaches which mostly require explicit guidance by a human expert, our drone learns how to make favorable shot type selections by experience. We propose a learning scheme which exploits aesthetical features of retrospective shots in order to extract a desirable policy for better prospective shots. We train our agent in realistic AirSim simulations using both hand-crafted and human reward functions. We deploy the same agent on a real DJI M210 drone in order to test generalization capability of our approach to real world conditions. To evaluate the success of our approach in the end, we conduct a comprehensive user study in which participants rate the shots taken using our method and write comments about them.


Artificial Intelligence, a Primer for Novices (like me) - #LatinaGeeks

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I have become obsessed with artificial intelligence over the past few months (also known as AI). It is of particular interest because living in Silicon Valley provides access to many bleeding edge experiences in technological innovation. Self-driving cars are on our roads, robotic restaurants serve us our food, and even barista robots make our coffee. This new technologically enhanced world is upon us. So rather than fight the inevitable, I find it more productive to seek to understand and to consider the impact of these new technologies in my life and society.


WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving

arXiv.org Machine Learning

Machine learning can provide efficient solutions to the complex problems encountered in autonomous driving, but ensuring their safety remains a challenge. A number of authors have attempted to address this issue, but there are few publicly-available tools to adequately explore the trade-offs between functionality, scalability, and safety. We thus present WiseMove, a software framework to investigate safe deep reinforcement learning in the context of motion planning for autonomous driving. WiseMove adopts a modular learning architecture that suits our current research questions and can be adapted to new technologies and new questions. We present the details of WiseMove, demonstrate its use on a common traffic scenario, and describe how we use it in our ongoing safe learning research.