Obstacle Detection Quality as a Problem-Oriented Approach to Stereo Vision Algorithms Estimation in Road Situation Analysis
This work addresses the challenge of efficiently assessing obstacle detection in autonomous driving, though it appears incremental as it focuses on evaluation rather than new detection techniques.
The paper tackles the problem of evaluating stereo vision obstacle detection algorithms for road situations by introducing a method that reduces the effort needed to prepare test datasets, with results applicable to self-driving cars and driver assistance systems.
In this work we present a method for performance evaluation of stereo vision based obstacle detection techniques that takes into account the specifics of road situation analysis to minimize the effort required to prepare a test dataset. This approach has been designed to be implemented in systems such as self-driving cars or driver assistance and can also be used as problem-oriented quality criterion for evaluation of stereo vision algorithms.