Category: Automotive

To remain competitive in a marketplace that increasingly views transportation as a service emphasizing customer experience, vehicle capabilities and mobility applications need to improve and increase value over time, much like the internet of things and smart phones have done. Vehicle manufacturers and fleet operators are responding to this change by using data to inform […]

This post was co-written by Hendrik Schoeneberg, Sr. Global Big Data Architect, The An Binh Nguyen, Product Owner for Cloud Simulation at Continental, Autonomous Mobility – Engineering Platform, Rumeshkrishnan Mohan, Global Big Data Architect, and Junjie Tang, Principal Consultant at AWS Professional Services. AV/ADAS simulations processing large-scale field sensor data such as radar, lidar, and […]

This post was co-written by Martin Stamm, Principal Expert SW Architecture at Continental Automotive, Andreas Falkenberg, Senior Consultant at AWS Professional Services, Daniel Krumpholz, Engagement Manager at AWS Professional Services, David Crescence, Sr. Engagement Manager at AWS, and Junjie Tang, Principal Consultant at AWS Professional Services. Automakers are embarking on a digital transformation journey to […]

This post was co-written by Pawel Kowalski, a Technical Product Manager for DXC RoboticDrive and Dr. Max Böhm, a software and systems architect and DXC Distinguished Engineer. To build the first fully autonomous vehicle, L5 standard per SAE, auto-manufacturers collected sensor data from test vehicle fleets across the globe in their testing facilities and driving […]

Many aspects of autonomous driving (AD) system development are based on data that capture real-life driving scenarios. Therefore, research and development professionals working on AD systems need to handle an ever-changing array of interesting datasets composed from the real-life driving data.  In this blog post, we address a key problem in AD system development, which […]

In the automotive industry, ROS bag files are frequently used to capture drive data from test vehicles configured with cameras, LIDAR, GPS, and other input devices. The data for each device is stored as a topic in the ROS bag file. Developers and engineers need to visualize and inspect the contents of ROS bag files to identify […]

This Field Notes blog post in 2020 explains how to build an Autonomous Driving Data Lake using this Reference Architecture. Many organizations face the challenge of ingesting, transforming, labeling, and cataloging massive amounts of data to develop automated driving systems. In this re:Invent session, we explored an architecture to solve this problem using Amazon EMR, Amazon […]

Cloud Architects developing autonomous driving and ADAS workflows are challenged by loosely distributed process steps along the tool chain in hybrid environments. This is accelerated by the need to create a holistic view of all running pipelines and jobs. Common challenges include: finding and getting access to the data sources specific to your use case, […]

In this blog post, we demonstrate how to build an automated and scalable data pipeline for autonomous driving. This solution was built with the goal of accelerating the process of analyzing recorded footage and training a model to improve the experience of autonomous driving. We will demonstrate the extraction of images from ROS bag file […]

This post was co-written by Amr Ragab, AWS Sr. Solutions Architect, EC2 Engineering and Anant Nawalgaria, former AWS Professional Services EMEA. One of the most common needs we have heard from customers in Autonomous Vehicle (AV) development, is to launch a hybrid deployment environment at scale. As vehicle fleets are deployed across the globe, they […]