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In the automotive industry, multiple prototypes are used for vehicle development purposes. These prototypes are typically put through rigorous testing, both under accelerated and real world conditions, to ensure that all the problems related to design, manufacturing, process etc. are identified and solved before it reaches the hands of the customer. One of the challenges faced in testing, is the low repeatability of the real world tests. This may be predominantly due to changes in the test conditions over a period of time like road, traffic, climate etc. Estimating the repeatability of a real world test has been difficult due to the complex and multiple parameters that are usually involved in a vehicle level test and the time correlation between different runs of a real world test does not exist. In such a scenario, the popular and the well-known univariate correlation methods do not yield the best results.

In the recent years, the timeline of releasing a new vehicle has decreased drastically due to rapidly changing trends in the automotive industry. Therefore, it is very important to constantly optimize the development phases, starting from concept initiation to the final testing of production ready vehicle. The real world tests conducted on vehicles take huge amount of time, since these tests are carried out for large kilometers to periodically analyze tire wear, clutch wear and brake failure. Collecting large kilometers of CAN data is also tedious and time consuming due to various unwanted variables which add up during real world tests. In this paper, a technique known as Rescaled Range Analysis is adapted to abridge the collection of kilometers data from testing by nearly ten times. This analysis estimates a Hurst coefficient to correlate the entire data with its divided parts. The division factor of the entire data is very crucial for the analysis.