AWS for Industries
Reimagining B-Pillar DFMEA: Why Ontology-Grounded AI Is the Future of Automotive Engineering
This two-part series explores how ontology-grounded agentic AI transforms Design Failure Mode and Effects Analysis (DFMEA) for safety-critical automotive components — from the strategic imperative driving adoption to the architectural patterns and implementation details. In this post, we focus on: How AI can help with DFMEA, how engineering ontologies enable AI to reason for failure mechanisms rather than pattern-match, and what engineering leaders should prioritize today.
Migrate and Archive data for ADAS workloads on AWS
Background Autonomous driving and advanced driver assistance systems (ADASs) require the processing of large and complex workloads in near real time. These workloads typically include tasks such as object detection and classification, lane detection, sensor fusion, path planning, and decision-making. The data from various sensors needs to be processed rapidly to enable the vehicle to […]
Connecting and Authenticating Automotive iOS App to AWS IoT Core
Introduction Connecting car applications to the cloud for exchanging Internet of Things (IoT) data enables several benefits for automotive vehicles. Authenticating the connecting securely is crucial for enabling real-time diagnostics and predictive maintenance through continuous telemetry data sent to the cloud by original equipment manufacturer (OEM) for monitoring. An OEM’s ability to provide over-the-air updates […]


