Category: Automotive

Software engineering teams building for RISC-V architectures often wait months for hardware prototypes before they can start development and evaluations. This post shows how MachineWare’s SIM-V — an ultra-fast RISC-V Virtual Platform — runs on Amazon Web Services, Inc. (AWS), so software teams can develop, debug, and validate RISC-V software long before silicon is available. […]

Agentic blockchain-based payments are poised to transform in-vehicle driving experiences. As vehicles become increasingly connected and autonomous, they are evolving from passive transportation tools into active economic agents capable of conducting their own financial transactions [see HBR Article, 2021]. Imagine your car automatically paying for highway tolls, electric charging sessions, parking fees, or even purchasing […]

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.

This post presents a new Kiro Power called Arm SoC Migration Power helping developers migrate codebases between Arm SoCs. The Kiro Arm SoC Migration Power is released as a collaboration of Arm and AWS.

In this blog post, we show how Rivian, a leading innovator in the electric vehicle market, is using this feature to support modern containerized workloads and highly available database architectures for their critical manufacturing workloads at the edge.

This blog post covers how Toyota deployed HiveMQ on Amazon ECS with mutual TLS (mTLS) for a secure, scalable IIoT architecture, now scaling beyond a successful single-plant pilot across all North American facilities.

In this post, we explore how SourceFS from Source.dev, running on AWS, transforms the AOSP build experience – reducing end-to-end checkout-and-build time from 3 hours to under 5 minutes. We highlight how leading automotive OEMs are achieving material gains in build velocity, cost efficiency and developer productivity by using SourceFS.

In this blog post, we demonstrate how to build a serverless supply chain management solution tailored for automotive customers using AWS AppSync (a managed GraphQL service) and Amazon Aurora Serverless (an on-demand, auto-scaling relational database). This solution addresses common challenges in managing parts inventory, orders, and shipments by using a fully serverless, GraphQL-based approach.

This blog will cover how PQD enables the transformation of after-sales vehicle quality from a reactive to a proactive, data-driven approach enabled by connected vehicle data, software-defined architectures, and AI/ML services from AWS.

In this blog post, we’ll explain why BMW Group chose Amazon Web Services, Inc. (“AWS”) as its cloud provider to deliver this user experience and dive deep into how the team solved the technical challenges along the way.