Long-Term Vision
Last updated
Last updated
The long-term vision for SDVs focuses on a 100% virtualized vehicle in the cloud, enabling engineers to clone test vehicles effortlessly. Instead of spending months replicating complex HIL integration scenarios or building physical test vehicles, virtual vehicles can be copied and pasted with a few clicks. This allows for rapid deployment of identical test setups, accelerating testing timelines and reducing costs.
Cloning virtual vehicles provides a significant edge in scalability. Engineers can create unlimited instances of the same test setup, whether for running simultaneous tests, replicating advanced driving scenarios, or supporting globally distributed teams. This capability removes constraints associated with physical vehicle availability, enabling unparalleled flexibility in testing.
Generative AI simplifies reconfiguration of virtual vehicles. For example, converting a left-hand drive model to a right-hand drive becomes seamless as AI identifies and automates adjustments, such as repositioning the steering wheel and associated components. This results in two independent test environments for parallel validation, enhancing efficiency in managing diverse vehicle variants.
Virtual environments pave the way for HIL testing, where real hardware components, such as ECUs, sensors, and actuators, are validated within simulated conditions. Initially, Component HIL validates single hardware modules, while System HIL scales to the entire vehicle, forming a House of HIL for comprehensive testing.
The House of HIL integrates dozens of ECUs, sensors, and actuators in modular test racks, supporting system-level validation. Reconfiguring these physical setups for new variants—like left- versus right-hand drive—requires significant time and resources. However, feeding simulated data into HIL environments ensures robust safety validation without needing operational vehicles.
Features like “cabin door open” require safety checks, such as vehicle speed and rear-camera inputs. Simulated data replaces real-world inputs, enabling hardware systems to operate and validate functions within the HIL lab seamlessly.
As SDVs transition toward centralized compute and zone-based architectures, the complexity of HIL systems may reduce. Combined with the ability to clone virtual vehicles, this evolution allows for a modular, scalable testing strategy that enhances speed, cost efficiency, and collaboration.
By integrating virtual cloning, AI-driven configuration, and HIL validation, this approach empowers the industry to accelerate development cycles and streamline the path to software-defined vehicles.
