Test Concrete Scenarios Extraction of Lane-Changing Scenarios Based
on China-FOT Naturalistic Driving Data 2023-01-7055
On account of the insufficient lane-changing scenario test cases and the
inability to conduct graded evaluation testing in current autonomous driving
system field testing, this paper proposed an approach that combined data-driven
and knowledge-driven methods to extract lane-changing test concrete scenarios
with graded risk levels for field testing. Firstly, an analysis of the
potentially hazardous areas in lane-changing scenarios was conducted to derive
key functional lane-changing scenarios. Three typical key functional
lane-changing scenarios were selected, namely, lane-changing with a preceding
vehicle braking, lane-changing with a preceding vehicle in the same direction,
and lane-changing with a rear cruising vehicle in the adjacent lane, and their
corresponding safety goals were respectively analyzed. Secondly, the GAMAB
criterion was introduced as an evaluation standard for autonomous driving
systems. By utilizing lane-changing scenario data selected from the China-FOT
naturalistic driving data, a scenario risk classification model and a model for
excellent driver response performance in lane-changing scenarios were
established. Finally, concrete scenarios corresponding to different risk levels
for each type of lane-changing scenario were extracted through simulation. Test
concrete cases for field testing were selected at the risk boundaries based on
the characteristics of China-FOT naturalistic driving data. The results
demonstrated that the proposed approach was capable of effectively extracting
701 high-risk scenarios and 446 medium-risk scenarios from a pool of 9000
concrete scenarios based on key functional lane-changing scenarios. Furthermore,
representative lane-changing test concrete cases can be selected from the risk
boundaries. This approach enabled a graded evaluation of the lane-changing
capability of the autonomous driving system.