Real-world data is crucial for in-cabin sensing because it provides information about the actual conditions and situations that a sensor may encounter, whilst simulated data allows for controlled and repeatable testing in a variety of scenarios that may be difficult to replicate in real-world testing. Combining real-world in-cabin data and simulated scenarios can be done through a process called data fusion. Once the real-world in-cabin data has been merged with the simulated scenarios, the dataset can be analyzed to gain insights and develop solutions, providing a more comprehensive understanding of vehicle performance and driver behavior.