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autonomous vehicle

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Published By: Dell EMC Storage     Published Date: Mar 27, 2019
The automotive industry is in the midst of a highly competitive transitional period, with the ultimate goal of fully autonomous or “driverless” vehicles likely to be realized within a decade. The scale and intensity at which OEMs and Tier 1 suppliers must bring innovations to market – while containing costs, mitigating risks, managing product complexity and maintaining compliance – is challenging. The emergence of Advanced Driver Assistance Systems (ADAS), designed to enhance passenger, vehicle and road safety, introduces disruptive requirements for engineering IT infrastructure – particularly storage, where even entrylevel capacities are measured in petabytes. This paper will explore the infrastructure challenges facing OEMs and Tier 1 suppliers in developing and validating ADAS technologies, and propose a storage solution that is optimized for such workloads, delivering high performance, high concurrency and massive scalability.
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Dell EMC Storage
Published By: Dell EMC     Published Date: Mar 27, 2019
The automotive industry is in the midst of a highly competitive transitional period, with the ultimate goal of fully autonomous or “driverless” vehicles likely to be realized within a decade. The scale and intensity at which OEMs and Tier 1 suppliers must bring innovations to market – while containing costs, mitigating risks, managing product complexity and maintaining compliance – is challenging. The emergence of Advanced Driver Assistance Systems (ADAS), designed to enhance passenger, vehicle and road safety, introduces disruptive requirements for engineering IT infrastructure – particularly storage, where even entrylevel capacities are measured in petabytes. This paper will explore the infrastructure challenges facing OEMs and Tier 1 suppliers in developing and validating ADAS technologies, and propose a storage solution that is optimized for such workloads, delivering high performance, high concurrency and massive scalability.
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Dell EMC
Published By: Intel     Published Date: Apr 11, 2019
To remain competitive, manufacturers must focus on achieving new growth while driving down costs. Key to achieving this is greater flexibility and a dramatic upturn in operational efficiency across the manufacturing process. One area ripe for improvement is intralogistics transportation. Many manufacturers still rely on autonomous guide vehicles (AGVs) to undertake repetitive transport tasks; but, rigid in nature, they do not support today’s demand-driven, dynamic manufacturing environments. Intelligent autonomous mobile robots (AMRs), like SEIT* from Milvus Robotics, offer a viable and cost-effective alternative. This solution brief describes how to solve business challenges through investment in innovative technologies.
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Intel
Published By: HERE Technologies     Published Date: Aug 12, 2019
More sophisticated cameras and vehicle sensors are enabling new ADAS features and the deployment of highly autonomous vehicles. However, reactive decisions and camera-based systems struggle when lane markings fade, snow or dirt covers the road, and the environment changes. Map-based Lane Keeping with HERE HD Live Map from VSI Labs examines how HD map assets can improve the safety and performance of automated vehicle features. Download this free report to learn: • How map data improves the performance and safety of ADAS features • How map-based systems outperform computer vision only system • The architecture of VSI’s map-based lane keeping system
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HERE Technologies
Published By: Dassault Systèmes     Published Date: May 01, 2019
The Cloud Opportunity is Compelling Cloud solutions offer benefits along implementation, operational, and business dimensions. They provide companies the flexibility and agility they need to compete in today’s changing Automotive market as old and new players alike try to capitalize on the transition to electric, autonomous vehicles. Companies are not willing to trade off the PIP features that directly impact their success drivers – quality, innovation, reliability, performance, and product cost.
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Dassault Systèmes
Published By: Milliman     Published Date: Sep 07, 2016
Read this whitepaper to learn why manufacturers of autonomous vehicles should be making a Supplier Product Liability Autonomous Share (SPLASh) pool to manage risk.
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autonomous vehicles, splash, risk management, auto market, original equipment manufacturers, insurance risk, insurance
    
Milliman
Published By: Pure Storage     Published Date: Dec 05, 2018
Advances in deep neural networks have ignited a new wave of algorithms and tools for data scientists to tap into their data with artificial intelligence (AI). With improved algorithms, larger data sets, and frameworks such as TensorFlow, data scientists are tackling new use cases like autonomous driving vehicles and natural language processing. Read this technical white paper to learn reasons for and benefits of an end-to-end training system. It also shows performance benchmarks based on a system that combines the NVIDIA® DGX-1™, a multi-GPU server purpose-built for deep learning applications and FlashBlade, a scale-out, high performance, dynamic data hub for the entire AI data pipeline.
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Pure Storage
Published By: Swift Navigation     Published Date: Feb 01, 2017
Download the Free White Paper: Piksi Multi for Autonomous Vehicles to learn how Swift Navigation’s centimeter-level accurate GNSS solution improves the performance of autonomous vehicles. Piksi Multi is a centimeter-accurate RTK GPS receiver designed for easy integration in the automotive core sensor suite. Its multiple bands and satellite constellations improve robustness of autonomous vehicle functionality with 99th percentile accuracy and convergence times in seconds.
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rtk, gps, gnss, autonomous vehicle, centimeter-accuracy, multi-band, multi-constellation, l1/ l2, automotive sensors, level 3+ autonomy, improve safety, automakers, navigation oems, electronics
    
Swift Navigation
Published By: Pure Storage     Published Date: Jul 03, 2019
Advances in deep neural networks have ignited a new wave of algorithms and tools for data scientists to tap into their data with artificial intelligence (AI). With improved algorithms, larger data sets, and frameworks such as TensorFlow, data scientists are tackling new use cases like autonomous driving vehicles and natural language processing.
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Pure Storage
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