  Environment perception, including object detection and distance estimation,
is one of the most crucial tasks for autonomous driving. Many attentions have
been paid on the object detection task, but distance estimation only arouse few
interests in the computer vision community. Observing that the traditional
inverse perspective mapping algorithm performs poorly for objects far away from
the camera or on the curved road, in this paper, we address the challenging
distance estimation problem by developing the first end-to-end learning-based
model to directly predict distances for given objects in the images. Besides
the introduction of a learning-based base model, we further design an enhanced
model with a keypoint regressor, where a projection loss is defined to enforce
a better distance estimation, especially for objects close to the camera. To
facilitate the research on this task, we construct the extented KITTI and
nuScenes (mini) object detection datasets with a distance for each object. Our
experiments demonstrate that our proposed methods outperform alternative
approaches (e.g., the traditional IPM, SVR) on object-specific distance
estimation, particularly for the challenging cases that objects are on a curved
road. Moreover, the performance margin implies the effectiveness of our
enhanced method.
