  Human-vehicle cooperative driving has become the critical technology of
autonomous driving, which reduces the workload of human drivers. However, the
complex and uncertain road environments bring great challenges to the visual
perception of cooperative systems. And the perception characteristics of
autonomous driving differ from manual driving a lot. To enhance the visual
perception capability of human-vehicle cooperative driving, this paper proposed
a cooperative visual perception model. 506 images of complex road and traffic
scenarios were collected as the data source. Then this paper improved the
object detection algorithm of autonomous vehicles. The mean perception accuracy
of traffic elements reached 75.52%. By the image fusion method, the gaze points
of human drivers were fused with vehicles' monitoring screens. Results revealed
that cooperative visual perception could reflect the riskiest zone and predict
the trajectory of conflict objects more precisely. The findings can be applied
in improving the visual perception algorithms and providing accurate data for
planning and control.
