I'm curious, how does sensor fusion—the process of combining data from different sensors (cameras, LiDAR, radar, ultrasonics)—work in autonomous systems? How do these systems make accurate decisions even with noisy data? What are the core algorithms behind this process?
How does sensor fusion work in autonomous vehicles?
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Sensor fusion is all about combining multiple sensors to get a more robust and precise picture of the surroundings—like a human driver who doesn’t just rely on their eyesight but also their hearing and the vibrations of the vehicle.
In practice, this usually involves filters like the **Kalman Filter** or its extensions (e.g., the **Extended Kalman Filter** for nonlinear systems). Take an example: your car uses a camera for color detection (e.g., traffic lights), LiDAR for precise distance measurement (parking), and radar for speed measurements (overtaking maneuvers). The fusion combines this data by weighting positions—on a rainy day, camera data is less trustworthy than LiDAR or radar. For real-time applications, **Bayesian networks** or **particle filter-based approaches** are often used to filter out outliers. My brother, who worked on an autonomous golf caddy project, swears by the combination of EKF and probabilistic modeling—especially in bad weather, where a single sensor fails.