A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving

Understand

Capturing uncertainty in object detection is indispensable for safe autonomous driving.

• In recent years, deep learning has become the de-facto approach for object detection, and many probabilistic object detectors have been proposed.
• However, there is no summary on uncertainty estimation in deep object detection, and existing methods are not only built with different network architectures and uncertainty estimation methods, but also evaluated on different datasets with a wide range of evaluation metrics.
• As a result, a comparison among methods remains challenging, as does the selection of a model that best suits a particular application.