Vision-based Navigation of Autonomous Vehicle in Roadway Environments with Unexpected Hazards

arXiv.org Artificial Intelligence

ABSTRACT Vision-based navigation of modern autonomous vehicles primarily depends on Deep Neural Network (DNN) based systems in which the controller obtains input from sensors/detectors such as cameras, and produces an output such as a steering wheel angle to navigate the vehicle safely in roadway traffic. Typically, these DNN-based systems are trained through supervised and/or transfer learning; however, recent studies show that these systems can be compromised by perturbation or adversarial input features on the trained DNN-based models. Similarly, this perturbation can be introduced into the autonomous vehicle DNN-based system by roadway hazards such as debris and roadblocks. In this study, we first introduce a roadway hazardous environment (both intentional and unintentional) that can compromise the DNN-based system of an autonomous vehicle, producing an incorrect vehicle navigational output such as a steering wheel angle, which can cause crashes resulting in fatality and injury. Then, we develop an approach based on object detection and semantic segmentation to mitigate the adverse effect of this hazardous environment, one that helps the autonomous vehicle to navigate safely around such hazards. This study finds the DNN-based model with hazardous object detection, and semantic segmentation improves the ability of an autonomous vehicle to avoid potential crashes by 21% compared to the traditional DNN-based autonomous driving system.


To Compete With New Rivals, Chipmaker Nvidia Shares Its Secrets

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Five years ago, Nvidia was best known as a maker of chips to power videogame graphics in PCs. Then researchers found its graphics chips were also good at powering deep learning, the software technique behind recent enthusiasm for artificial intelligence. The discovery made Nvidia into the preferred seller of shovels for the AI gold rush that's propelling dreams of self-driving cars, delivery drones and software that plays doctor. The company's stock-market value has risen 10-fold in three years, to more than $100 billion. That's made Nvidia and the market it more-or-less stumbled into an attractive target.


To Compete With New Rivals, Chipmaker Nvidia Shares Its Secrets

WIRED

Five years ago, Nvidia was best known as a maker of chips to power videogame graphics in PCs. Then researchers found its graphics chips were also good at powering deep learning, the software technique behind recent enthusiasm for artificial intelligence. The discovery made Nvidia into the preferred seller of shovels for the AI gold rush that's propelling dreams of self-driving cars, delivery drones and software that plays doctor. The company's stock-market value has risen 10-fold in three years, to more than $100 billion. That's made Nvidia and the market it more-or-less stumbled into an attractive target.


To keep up its freakish growth, Nvidia needs to convince the world it's a leader in AI

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Investors have smiled on Nvidia's efforts to capitalize on the burgeoning market for artificial-intelligence supercomputers and affordable graphics processor units: Following 220% growth last year, the company's stock is currently hovering at an all-time high. Nvidia's challenge now is to sustain that momentum, especially as competitors like AMD and Intel make significant advances. Analysts say the key is for Nvidia to maintain focus on its two biggest growth areas: artificial intelligence and self-driving cars. The recent artificial-intelligence boom couldn't have been better for Nvidia: Starting in the late 2000s, AI researchers discovered that GPUs were perfect for their massively complex deep-learning algorithms, initially providing up to 70x (pdf) better performance than traditional CPUs. Nvidia obliged the community, building a set of tools to make running those algorithms even easier and faster.


Artificial Intelligence Is (and Isn't) Transforming Radiology

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Radiologists say that using AI can make their practice better without rendering them obsolete. Artificial intelligence conjures up scenarios of robots building other robots or self-driving vehicles putting truck drivers out of work. But these days, IBM's Watson computer is just as likely to interpret a CT scan, using AI to revolutionize radiology and other medical fields. Researchers believe AI will not put human radiologists on the endangered species list anytime soon, if ever. But AI and deep learning offer speed, accuracy and consistency to extend the capabilities of human imaging professionals.