Extended Existence Probability Using Digital Maps for Object Verification
This work addresses safety-critical false positives in automated driving perception, though it is incremental as it builds on existing tracking methods with a new verification module.
The paper tackles the problem of false object detections in multi-object tracking for automated vehicles by introducing a probabilistic model that uses digital map elements to verify object existence, reducing false positives while maintaining true positives in urban scenarios.
A main task for automated vehicles is an accurate and robust environment perception. Especially, an error-free detection and modeling of other traffic participants is of great importance to drive safely in any situation. For this purpose, multi-object tracking algorithms, based on object detections from raw sensor measurements, are commonly used. However, false object hypotheses can occur due to a high density of different traffic participants in complex, arbitrary scenarios. For this reason, the presented approach introduces a probabilistic model to verify the existence of a tracked object. Therefore, an object verification module is introduced, where the influences of multiple digital map elements on a track's existence are evaluated. Finally, a probabilistic model fuses the various influences and estimates an extended existence probability for every track. In addition, a Bayes Net is implemented as directed graphical model to highlight this work's expandability. The presented approach, reduces the number of false positives, while retaining true positives. Real world data is used to evaluate and to highlight the benefits of the presented approach, especially in urban scenarios.