  Autonomous Vehicles (AV)'s wide-scale deployment appears imminent despite
many safety challenges yet to be resolved. The modern autonomous vehicles will
undoubtedly include machine learning and probabilistic techniques that add
significant complexity to the traditional verification and validation methods.
Road testing is essential before the deployment, but scenarios are repeatable,
and it's hard to collect challenging events. Exploring numerous, diverse and
crucial scenarios is a time-consuming and expensive approach. The research
community and industry have widely accepted scenario-based testing in the last
few years. As it is focused directly on the relevant critical road situations,
it can reduce the effort required in testing. The scenario-based testing in
simulation requires the realistic behaviour of the traffic participants to
assess the System Under Test (SUT). It is essential to capture the scenarios
from the real world to encode the behaviour of actual traffic participants.
This paper proposes a novel scenario extraction method to capture the lane
change scenarios using point-cloud data and object tracking information. This
method enables fully automatic scenario extraction compared to similar
approaches in this area. The generated scenarios are represented in OpenX
format to reuse them in the SUT evaluation easily. The motivation of this
framework is to build a validation dataset to generate many critical concrete
scenarios. The code is available online at
https://github.com/dkarunakaran/scenario_extraction_framework.
