In recent years, the fusion of millimeter-wave radar and vision has emerged as a prominent research hotspot and a mainstream solution for autonomous driving perception. This integration spans multiple hierarchical levels, and the evolution of each level is not an isolated technological advancement, but rather a synergistic outcome driven by technological maturity, computational constraints, and mass-production requirements. Despite the inherent information loss associated with decision-level fusion, it remains the predominant engineering approach in the industry due to its superior functional safety and cost-effectiveness. Conversely, feature-level fusion has developed rapidly, propelled by a positive feedback loop of deep learning, bird’s-eye view (BEV) representations, and cross-modal attention mechanisms, moving beyond exclusive reliance on the Transformer architecture. Meanwhile, data-level fusion directly integrates raw radar point clouds and image pixels, a strategy that theoretically minimizes information loss. However, its large-scale deployment in practical engineering applications is hindered by critical bottlenecks, including poor interpretability, vulnerability to cross-sensor fault propagation, and severe challenges in safety isolation. From an engineering perspective, this paper systematically analyzes the evolutionary trajectory of millimeter-wave radar and vision fusion technologies, clarifying the parallel coexistence and adaptive deployment of these three fusion levels in practical autonomous driving scenarios.
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