Increasing levels of driving automation in more complex ODDs demand effective ways to continuously develop and verify software across millions of virtual miles and thousands of driving scenarios. As a result, scalable MLOps and DevOps have become the engineering foundations underpinning those at the leading edge of AD technology. In this presentation, Arm and AWS join forces to discuss how cloud-to-car environmental parity can enable better testing coverage and faster time-to-market across the industry. Based on a practical SOAFEE-based case study, the presenters will explore the impact of adding the image signal processor (ISP) model into the virtual perception pipeline, and how a similar methodology can benefit OEMs, Tier 1 suppliers and AD software vendors.

• The pros and cons of different virtual ECU abstraction levels
• The advantages of cloud-to-car environmental parity for software development and verification
• Why images tuned for human vision may not yield best results for compute vision
• The importance of integrating a virtual model of the ISP into both AI training and testing stages