Sensor Fusion — It’s all about Prediction

Challenging scenario for the protection of pedestrians inspired by the NCAP AEB test catalog. The pedestrian must be recognized as endangered before he or she enters the road so that the vehicle can initiate a harmless emergency stop. Predicting the pedestrian’s motion using different behavior assumptions is a crucial requirement for the sensor fusion system.

Sensor Fusion in a Nutshell

However, the main task of any sensor fusion system is to compare the sensor observations or measurements with the system’s expectation of these measurements. From the differences, the system’s state gets adapted or updated.

The prediction quality is essential for the association and thus for the overall sensor fusion performance.

The Right Number of Models

Using a separate model for each object class can significantly increase the sensor fusion performance as track predictions get better and thus, the association gets better.

With such a Multiple Model approach, it becomes more likely that the correct measurements get associated with the track.

Handling all hypotheses correctly and efficiently is a complex task that needs to be addressed by modern sensor fusion systems.

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