Texas Instruments

This article was originally published at Texas Instruments' website. It is reprinted here with the permission of Texas Instruments. The robustness and reliability of a self-driving car’s computer vision system has received a lot of  news coverage. As a vision software engineer at TI helping customers implement advanced driver assistance systems (ADAS) on our TDAx

This article was originally published at Texas Instruments’ website (PDF). It is reprinted here with the permission of Texas Instruments. Introduction By September of 2013, Google’s self-driving car had completed over 500,000 miles of driving without a single accident under computer control[1]. Google’s disruptive driver- less car project was aimed to improve car safety and

This article was originally published at Texas Instruments’ website (PDF). It is reprinted here with the permission of Texas Instruments. Introduction Cars continue to become smarter and smarter, integrating new and cutting-edge technologies to make the driving experience safer and more enjoyable. With the goal of reducing roadway fatalities, enabling these new advanced driver assistance

This article was originally published at Texas Instruments’ website (PDF). It is reprinted here with the permission of Texas Instruments. Introduction There were 1.2 million global traffic deaths in 20101. 93 percent of traffic accidents in the US are due to human error, typically due to inattention2. ADAS (Advanced Driver Assistance Systems) applications such as

This article was originally published as a two-part blog series at Aldec's website. It is reprinted here with the permission of Aldec. It's pretty hard to overestimate the role of heterogeneous embedded systems based on Xilinx® Zynq®-7000 All-Programmable devices in tasks like computer vision. Many consumer electronics and specialized devices are emerging to facilitate and

This article was originally published at Texas Instruments’ website (PDF). It is reprinted here with the permission of Texas Instruments. Introduction Computer-vision algorithms used to be quite different from one another. For example, one algorithm would use Hough transforms to detect lines and circles, whereas detecting objects of interest in images would require another technique

This article was originally published at Texas Instruments' website. It is reprinted here with the permission of Texas Instruments. Vehicles capable of autonomous operation are in the early stages of development today for use on the roads in the near future. To move self-driving cars from vision to reality, auto manufacturers depend on enabling electronic