[vsskanth]

From my narrow experience as an R&D engineer in the auto sector:

You need to understand the duty cycle of the platform you're developing for (ex. Tires for a vehicle). A garbage truck is going to have a different loading cycle from an EV or a CAT morning truck. If you know the right conditions your product will fail on durability.

Fleets are ok with collecting this data for R&D as long as you don't bother them with extra work and give them useful analytics. Data collection devices need to be easy to install, collect for a few weeks and remove.

You can also use this as an R&D platform to develop IoT data-enabled products like predictive maintenance, route analytics, insurance and warranty claims etc. ML has a lot of potential here but you need to collect data.

Eventually when you have an IoT product you can get a vendor to make an optimized ASIC to collect and process exactly what signals you want.

There are many more applications in the auto industry for these kind of niche products.