End-to-End Monocular Range Estimation for Forward Collision Warning.

Area of Science:

• Computer Vision
• Deep Learning
• Automotive Safety

Background:

• Forward collision warning (FCW) systems require accurate estimation of the range to the closest object.
• Existing monocular range estimation methods often fail with unseen object categories due to reliance on object-level annotations.
• Current methods involve sequential object detection and range estimation, limiting their applicability.

Purpose of the Study:

• To develop an end-to-end deep learning architecture for direct range estimation in FCW systems.
• To overcome limitations of category-specific and sequential methods for monocular range estimation.
• To create a system that generalizes to unseen object categories and diverse camera parameters.

Main Methods:

• An end-to-end deep learning architecture is proposed, representing target range as a weighted sum of potential distances.
• Potential distances are generated via inverse perspective projection using camera intrinsic and extrinsic parameters.
• A deep neural network predicts weights for these distances, optimizing directly for range estimation with target range supervision.

Main Results:

• The proposed method demonstrates generalization to objects with unseen categories, unlike previous approaches.
• The system effectively generalizes to images from different cameras and novel views by explicitly considering camera parameters.
• Experiments on synthetic and real-world data validate the method's performance and generalization capabilities.

Conclusions:

• The developed end-to-end deep learning architecture offers a robust solution for monocular range estimation in FCW systems.
• The method's ability to generalize to unseen categories and camera variations addresses key limitations of prior work.
• The approach provides partial interpretability through weight visualization, enhancing understanding of the estimation process.