  Recent Autonomous Vehicles (AV) technology includes machine learning and
probabilistic techniques that add significant complexity to the traditional
verification and validation methods. The research community and industry have
widely accepted scenario-based testing in the last few years. As it is focused
directly on the relevant crucial road situations, it can reduce the effort
required in testing. Encoding real-world traffic participants' behaviour is
essential to efficiently assess the System Under Test (SUT) in scenario-based
testing. So, it is necessary to capture the scenario parameters from the
real-world data that can model scenarios realistically in simulation. The
primary emphasis of the paper is to identify the list of meaningful parameters
that adequately model real-world lane-change scenarios. With these parameters,
it is possible to build a parameter space capable of generating a range of
challenging scenarios for AV testing efficiently. We validate our approach
using Root Mean Square Error(RMSE) to compare the scenarios generated using the
proposed parameters against the real-world trajectory data. In addition to
that, we demonstrate that adding a slight disturbance to a few scenario
parameters can generate different scenarios and utilise
Responsibility-Sensitive Safety (RSS) metric to measure the scenarios' risk.
