According to the 2018 National Highway Traffic Safety Administration (NHTSA) Traffic Safety Facts, in 2018, there were 857 fatal bicycle and motor vehicle crashes and an additional estimated 47,000 cycling injuries in the US . While motorists often accuse cyclists of being the cause of bike-car accidents, the analysis shows that this is not the case. The most common type of crash involved a motorist entering an intersection controlled by a stop sign or red light and either failing to stop properly or proceeding before it was safe to do so. The second most common crash type involved a motorist overtaking a cyclist unsafely. In fact, cyclists are the cause of less than 10% of bike-car accidents.
These are just a few examples of how artificial intelligence (AI) at the edge, combined with connected devices, could improve quality of life and business and help solve problems facing consumers and businesses today. A convergence of several overlapping technology trends is making new usages like these possible. Edge computing – another name for applications, data, and services located at the edge of a network rather than in a centralized datacenter – is poised to grow by 35 percent annually and become a $34 billion industry by 2023. Meanwhile, the development of human-aware AI systems and the deployment of AI technologies beyond the datacenter are huge opportunities thanks to the available compute power in today's systems. The benefits of AI at the edge are well demonstrated in the Smart Home, where the technology can help people manage the day-to-day running of the home and provide peace of mind and.
As companies welcome more autonomous robots and other heavy equipment into the workplace, we need to ensure equipment can operate safely around human teammates. In this post, we will show you how to build a virtual boundary with computer vision and AWS DeepLens, the AWS deep learning-enabled video camera designed for developers to learn machine learning (ML). Using the machine learning techniques in this post, you can build virtual boundaries for restricted areas that automatically shut down equipment or sound an alert when humans come close. For this project, you will train a custom object detection model with Amazon SageMaker and deploy the model to an AWS DeepLens device. Object detection is an ML algorithm that takes an image as input and identifies objects and their location within the image.