Sensor fusion combines data from cameras, LiDAR, radar, and other sensors into a unified perception pipeline for autonomous systems. Three main fusion strategies exist: early fusion (raw data combination), mid-level fusion (feature map combination), and late fusion (decision-level combination). Most real-world autonomous systems use a hybrid approach. Key algorithms include Kalman filter variants (EKF and UKF) for state estimation under nonlinear conditions, and probabilistic/confidence-weighted methods for resolving conflicting sensor readings. The article covers trade-offs in bandwidth, compute, modularity, and accuracy for each approach, with references to Nvidia's centralized radar architecture and Mobileye's True Redundancy concept.
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