End-to-end autonomous driving model based on multi-modal intermediate representations
An accurate understanding of the driving environment is one of the prerequisites for autonomous driving.In order to im-prove the scene understanding ability of autonomous driving vehicles,an end-to-end autonomous driving model based on semantic segmentation,horizontal disparity,and angle coding multi-modal intermediate representations was proposed.The end-to-end auton-omous driving model used deep learning technology to build perception-planning network.The perception network generated multi-modal intermediate representations with RGB and depth images as inputs to realize the spatial distribution description of road en-vironment and surrounding obstacles.The planning network used multi-modal intermediate representations to extract road environ-ment features and predict waypoints.Model training and performance testing were conducte based on the CARLA simulation plat-form.The results showed that the end-to-end autonomous driving model can realize the scene understanding of urban road environ-ment and effectively reduce collisions.Compared with the baseline model based on the single modal intermediate representation,its driving performance index is 31.47％better than the baseline model.
万方数据
维普
