AWS Autonomous Vehicle Feature Development Platform
Role
Senior UX Design Lead, Human-Centered Research Lead
Strategic Vision
As part ofAWS Industry Products, we partnered with leaders like Continental and Aurora to leverage AWS's global scale and cloud presence to accelerate Autonomous Vehicles (AV), Advanced Driver Assistance Systems (ADAS), and Autonomous Mobility (AM).
By bringing this work into the Applied AI Solutions group, we integratedAI agents and ML/AI efficienciesdirectly into the development lifecycle, enabling OEMs and Tier-1s to move faster from concept to production.
The Platform
A fully managed service for autonomous mobility and automated driving that ingests millions of miles of multi-sensor driving data at petabyte scale, with AI-driven data curation to find the most relevant 1% of data.
Through comprehensive stakeholder research with 60+ interviews across the autonomous vehicle ecosystem, we designed workflows that reduced operational overhead by ~40% and improved model accuracy by ~10%.
Platform Capabilities
End-to-end autonomous vehicle development lifecycle management
Vehicle Sensor Data Processing
Ingest millions of miles of multi-sensor driving data (camera, LiDAR, radar, ultrasonic) at petabyte scale. Our platform performs data curation, indexing, and advanced search withgenerative AI to find the most relevant 1% of data.
Enable simulation, reprocessing, and algorithm testing in a closed feedback loop with automated toolchain orchestration that reduces setup time from months to days.
AI Agents & Test Lifecycle
Embedded AI agents coordinate data flows, simulation scheduling, and toolchain updates. Support for Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) testing withADAS function validationagainst multiple parameters.
Automation removes repetitive operational overhead, ensures up-to-date processing pipelines, and increases iteration speed for feature development across L3–L5 autonomous mobility features.
Human-Centered Research & Design
Coordinated 60+ in-depth interviews with data wranglers, fleet managers, data engineers, data labelers, data scientists, algorithm developers, simulation engineers, and test engineers across the autonomous vehicle ecosystem.
Key findings revealed lack of data visibility across siloed tools, limited automation forcing manual work, and operational overload. Used these insights to design workflows that improved visibility, automation, and cross-team alignment.
End-to-End Workflow
From data ingestion to production deployment for L3–L5 autonomous mobility
Learn more aboutAWS's automotive cloud infrastructureandAWS for Automotive solutions.