Automotive

Automotive Lighting: From “Vision” to “Driving Assistance”

Automotive, Market Analysis, Yole Group /

Outlines: Automotive lighting market is expected to reach US$38.8 billion by 2024, with 4,9% CAGR between 2018 and 2024. Evolution of lighting technologies enables new functionalities. ADAS vehicles: sensors integration is becoming mandatory. LiDAR integration: OEM s have several requirements at different levels. “Autonomous vehicle technologies have a direct impact on traditional vehicles market and […]

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Speeding Up Deep Learning Inference Using TensorFlow, ONNX, and TensorRT

Algorithms & Models, Articles, Automotive, NVIDIA, Processors, Software, Tools /

This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Starting with TensorRT 7.0,  the Universal Framework Format (UFF) is being deprecated. In this post, you learn how to deploy TensorFlow trained deep learning models using the new TensorFlow-ONNX-TensorRT workflow. Figure 1 shows the high-level workflow of TensorRT.

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“Market Analysis on SoCs for Imaging, Vision and Deep Learning in Automotive and Mobile Markets,” a Presentation from Yole Développement

Automotive, Market Analysis, Processors, Videos, Yole Group /

John Lorenz, Market and Technology Analyst for Computing and Software at Yole Développement, delivers the presentation “Market Analysis on SoCs for Imaging, Vision and Deep Learning in Automotive and Mobile Markets” at the Edge AI and Vision Alliance’s March 2020 Vision Industry and Technology Forum. Lorenz presents Yole Développement’s latest analysis on the evolution of

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Laser Focused: How Multi-View LidarNet Presents Rich Perspective for Self-Driving Cars

Automotive, Blog Posts, NVIDIA /

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Deep neural network takes a two-stage approach to address lidar processing challenges. Editor’s note: This is the latest post in our NVIDIA DRIVE Labs series, which takes an engineering-focused look at individual autonomous vehicle challenges and how

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IEEE to Define a Formal Model for Safe Automated Vehicle Decision-Making

Automotive, Intel, News /

What’s New: The Institute of Electrical and Electronics Engineers (IEEE) has approved a proposal to develop a standard for safety considerations in automated vehicle (AV) decision-making and named Intel Senior Principal Engineer Jack Weast to lead the workgroup. Participation in the workgroup is open to companies across the AV industry, and Weast hopes for broad

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“Improving the Safety and Performance of Automated Vehicles Through Precision Localization,” a Presentation from VSI Labs

Algorithms & Models, Automotive, Edge AI and Vision Alliance, Processors, Sensors and Cameras, Software, Summit 2019, Tools, Videos /

Phil Magney, founder of VSI Labs, presents the “Improving the Safety and Performance of Automated Vehicles Through Precision Localization” tutorial at the May 2019 Embedded Vision Summit. How does a self-driving car know where it is? Magney explains how autonomous vehicles localize themselves against their surroundings through the use of a variety of sensors along

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“Distance Estimation Solutions for ADAS and Automated Driving,” a Presentation from AImotive

AImotive, Algorithms & Models, Automotive, Processors, Sensors and Cameras, Software, Summit 2019, Tools, Videos /

Gergely Debreczeni, Chief Scientist at AImotive, presents the “Distance Estimation Solutions for ADAS and Automated Driving” tutorial at the May 2019 Embedded Vision Summit. Distance estimation is at the heart of automotive driver assistance systems (ADAS) and automated driving (AD). Simply stated, safe operation of vehicles requires robust distance estimation. Many different types of sensors

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“Can We Have Both Safety and Performance in AI for Autonomous Vehicles?,” a Presentation from Codeplay Software

Algorithms & Models, Automotive, Codeplay Software, Processors, Software, Summit 2019, Tools, Videos /

Andrew Richards, CEO and Co-founder of Codeplay Software, presents the “Can We Have Both Safety and Performance in AI for Autonomous Vehicles?” tutorial at the May 2019 Embedded Vision Summit. The need for ensuring safety in AI subsystems within autonomous vehicles is obvious. How to achieve it is not. Standard safety engineering tools are designed

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“DNN Challenges and Approaches for L4/L5 Autonomous Vehicles,” a Presentation from Graphcore

Algorithms & Models, Automotive, Graphcore, Processors, Sensors and Cameras, Software, Summit 2019, Tools, Videos /

Tom Wilson, Vice President of Automotive at Graphcore, presents the “DNN Challenges and Approaches for L4/L5 Autonomous Vehicles” tutorial at the May 2019 Embedded Vision Summit. The industry has made great strides in development of L4/L5 autonomous vehicles, but what’s available today falls far short of expectations set as recently as two to three years

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“Addressing Corner Cases in Embedded Computer Vision Applications,” a Presentation from Netradyne

Algorithms & Models, Automotive, Edge AI and Vision Alliance, Object Identification, Software, Summit 2019, Tools, Videos /

David Julian, CTO and Founder of Netradyne, presents the “Addressing Corner Cases in Embedded Computer Vision Applications” tutorial at the May 2019 Embedded Vision Summit. Many embedded vision applications require solutions that are robust in the face of very diverse real-world inputs. For example, in automotive applications, vision-based safety systems may encounter unusual configurations of

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Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

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