Context

Dainese develops protective equipment for motorcycle riders and racing applications. With D-Air®, the company created an electronic airbag platform integrated into racing suits and road gear. The system combines embedded software, motion sensors, GPS, and airbag deployment electronics to detect crash situations and activate the airbag at the correct moment.

The central challenge was validating complex safety algorithms under real-world conditions while avoiding dangerous or expensive physical tests. The system had to function reliably across crash scenarios, normal riding conditions, sensor failures, communication faults, and low-battery states. Dainese also wanted to leverage historical telemetry data from more than 15 years of MotoGP and road operation.

Value Thinking

Dainese treated operational telemetry as a long-term engineering asset. Data collected from MotoGP races and road operation was preserved and reused to validate future algorithm revisions. PrioPrioritize The organization consciously decided to optimize for deployment correctness and rider safety. Preventing misfires carried the same importance as detecting real crash events.

VrfVerify Safety validation moved from infrequent physical testing toward continuous automated verification. Every software change could trigger regression tests across historical crash and riding scenarios. EvoEvolve The system evolved continuously using real-world operational feedback. Historical datasets enabled algorithm refinement without reproducing dangerous physical scenarios. MonMonitor Operational behavior from racing and road usage fed directly into engineering decisions. Production usage became part of the development loop.

Architect for Flow

StrStructure Dainese separated algorithm validation from physical crash testing by introducing a simulation boundary around the embedded controller. This allowed software behavior to evolve independently from physical test execution. DsnDesign They used a miniHIL environment from Protos to simulate sensors, faults, low-battery conditions, communication behavior, and crash scenarios while executing the real firmware on the target microcontroller. VrfVerify Regression tests executed continuously throughout development instead of being deferred to late integration phases. Developers validated changes before integration using repeatable automated tests.

Real operational scenarios continuously informed algorithm updates and safety validation strategies. VrfVerify The complete test setup became compact and reproducible enough for engineers to use directly during development, including outside centralized laboratory environments. The architecture reduced dependence on scarce physical systems and enabled high-frequency software iteration without compromising safety requirements.

Outcome

Dainese established a continuous verification workflow for safety-critical embedded software. Algorithm changes could be validated rapidly against historical telemetry and simulated fault scenarios without depending on physical crash tests. This increased test coverage, improved repeatability, and reduced the effort required for certification-relevant validation activities.

The case demonstrates how operational data, automated regression testing, and simulation infrastructure can accelerate development of safety-critical cyberphysical systems without compromising safety requirements. Real-world telemetry became a reusable engineering asset that continuously informed software evolution and validation decisions.