Use Cases
Seven industry-led use cases apply, verify and demonstrate the AI4SDV tools in realistic engineering scenarios.
Each use case carries out exemplary software engineering activities that simulate realistic SDV development – and, in UC6, a non-automotive domain. The resulting software is meaningful for software-defined vehicles and targets value-driven outcomes such as reduced cycle times, reduced lead times and reduced defect rates.
UC1Trustworthy OTA Testing and Validation Orchestration for SDV
Lead: TRATON · Engineering phases: A, E, F
AI-assisted architectural analysis and over-the-air deployment of software updates. New software versions are validated in silent mode without affecting vehicle behaviour, while AI agents analyse telemetry data to detect anomalies and optimise update strategies.
UC2AI-driven Continuous Evolution for Enhanced Perception in Autonomous SDV Operations
Lead: AVL-TR · Engineering phases: A, C, D, E, F
Continuous improvement of camera-based perception for on-road and off-highway vehicles. AI-assisted CI/CT/CD pipelines automate training, deployment and optimisation of perception models using continuously collected real-world data.
UC3Secure Feedback-Driven Engineering
Lead: IAV · Engineering phases: A, B, D, F
A closed-loop feedback system that derives requirements and validates features from real-world driver behaviour, combining contextual data, privacy-preserving edge AI and agentic AI to shorten iteration cycles.
UC4SDV Augmented Vehicle-Human Interfaces
Lead: AMPERE · Engineering phases: B, C, E
Integration of third-party software and AI-based components for multimodal user interaction (vision, speech, touch), with hardware abstraction, code optimisation and robust modular update mechanisms.
UC5Intelligent and Adaptive SDV Navigation and Positioning
Lead: VALEO · Engineering phases: A, B, D
AI-assisted tools for ADAS, automated parking and autonomous charging, improving requirement quality, formalising timing constraints and increasing test coverage using LLMs and neural-network compression.
UC6Evaluation of SDV AI Tools for Lighting Lifecycle
Lead: SIGNIFY · Engineering phases: B, C, D
Transfers the AI4SDV tools to a non-automotive domain – lighting electronics – to show that AI-assisted engineering accelerates innovation and toolchain efficiency across sectors.
UC7Intelligent Configuration Assistant for SDV Middleware
Lead: QORIX · Engineering phases: A, B, C, D
AI-assisted middleware configuration with schema-aware validation, explainable AI feedback and historical data analysis, reducing complexity and preventing misconfigurations across OEMs and Tier-1 suppliers.
