An autonomous vehicle operates in a busy urban environment, navigating among moving cars, cyclists, and pedestrians. It uses radar, lidar, and camera sensors to gather raw positional and visual data in real time. The system requires highly accurate tracking of nearby objects and must account for the fact that sensor observations may not be independent, as several sensors observe the same object from different perspectives.
Which sensor fusion architecture is most appropriate for this scenario, and why?

Question

An autonomous vehicle operates in a busy urban environment, navigating among moving cars, cyclists, and pedestrians. It uses radar, lidar, and camera sensors to gather raw positional and visual data in real time. The system requires  highly accurate tracking of nearby objects and must account for the fact that sensor observations may not be independent, as several sensors observe the same object from different perspectives. 
 Which sensor fusion architecture is most appropriate for this scenario, and why?

Answer

Hybrid Fusion , because it minimises data bandwidth by fusing features locally while maintaining independent object classification across sensors.

Answer

Central-Level Fusion, because it combines raw measurement data from all sensors into a unified view, enabling precise tracking and handling of potentially dependent observations.

Answer

Sensor-Level Fusion , because each sensor can independently classify and track objects using its specialised capabilities, reducing the need for central processing.

Answer

Decision-Level Fusion , because it is sufficient to combine final object classifications from each sensor, minimising processing demands.

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An autonomous vehicle operates in a busy urban environment, navigating among mov...

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