  The complex driving environment brings great challenges to the visual
perception of autonomous vehicles. It's essential to extract clear and
explainable information from the complex road and traffic scenarios and offer
clues to decision and control. However, the previous scene explanation had been
implemented as a separate model. The black box model makes it difficult to
interpret the driving environment. It cannot detect comprehensive textual
information and requires a high computational load and time consumption. Thus,
this study proposed a comprehensive and efficient textual explanation model.
From 336k video frames of the driving environment, critical images of complex
road and traffic scenarios were selected into a dataset. Through transfer
learning, this study established an accurate and efficient segmentation model
to obtain the critical traffic elements in the environment. Based on the
XGBoost algorithm, a comprehensive model was developed. The model provided
textual information about states of traffic elements, the motion of conflict
objects, and scenario complexity. The approach was verified on the real-world
road. It improved the perception accuracy of critical traffic elements to
78.8%. The time consumption reached 13 minutes for each epoch, which was 11.5
times more efficient than the pre-trained network. The textual information
analyzed from the model was also accordant with reality. The findings offer
clear and explainable information about the complex driving environment, which
lays a foundation for subsequent decision and control. It can improve the
visual perception ability and enrich the prior knowledge and judgments of
complex traffic situations.
