The semantic segmentation task aims to classify each pixel in the input image into a specific category, such as road, vehicle, pedestrian, etc. The difficulty of this task lies in the need to deal with complex backgrounds and occlusions, ensuring that different types of objects can be accurately distinguished under various lighting conditions and weather environments. The input is usually one or more RGB images, and the output is a label map corresponding to the category to which each pixel of the image belongs. For example, in one image, roads are marked with one color, vehicles with another color, and pedestrians with a third color.

The depth estimation task aims to infer the distance of each pixel or region from the camera from a single image or a series of images. The difficulty lies in the need to recover three-dimensional information in the absence of stereoscopic vision, especially at long distances and in texture-poor scenes. The input is usually one or more RGB images, and the output is a depth map, where the value of each pixel represents the distance from the point to the camera. For example, in an image, foreground objects have lower pixel values ​​(indicating that they are closer) and background objects have higher pixel values ​​(indicating that they are farther away).

The goal of the object detection task is to identify and locate specific objects, such as vehicles, pedestrians, traffic signs, etc., from input images. The difficulty lies in the need to accurately detect targets at different scales, angles, and occlusion situations, and to distinguish different categories of objects. The input is usually one or more RGB images, and the output is a bounding box containing each detected object and its class label. For example, if a car is detected, the output includes not only the car's location information, but also its class label. Object detection requires the model to have good generalization ability and robustness to cope with complex and changeable driving environments.

The target tracking task is to track the motion trajectory of a specific target in consecutive video frames. The difficulty of this task lies in dealing with issues such as rapid movement, occlusion, and appearance changes of the target. The input is usually a series of consecutive image frames converted from the video, and the output is the position information of each object in each frame. For example, for a moving car, object tracking requires continuously tracking its position changes over a series of video frames. This requires the model to not only have the ability to identify targets, but also be able to handle the correlation of targets between different frames to ensure the coherence and accuracy of tracking.