Maneuver Recognition in Urban Environments with Multi-Stream Networks
To accomplish the task of highly automated driving it is necessary to get an understanding of the scene surrounding the autonomous vehicle. One step towards the problem of maneuver recognition in the field of intelligent vehicles is closely related to the task of action recognition of human actions. Approaches found in literature solve the problem of action recognition based on RGB and optical flow images for every-day activities. Multi-stream networks with 3D-convolutional layers are frequently used to solve this task. The fusion methods as well as the inputs vary in literature. Besides this, long short-term memory cells are often found in the area of action recognition. Compared to 3d-convolutions they are able to recognize long term motions.
This thesis discusses the question as to whether approaches for action recognition can be used in the field of maneuver recognition. To achieve this, different fusion methods for multi-stream networks as well as different input images were examined. Furthermore, the impact of the addition of a long short-term memory cell was tested and evaluated.