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In autonomous driving, the most commonly used multimodal sensors are cameras, radar, and LiDAR. While LiDAR is known for providing detailed geometric information, it is expensive and requires regular maintenance for consumer vehicle. On the other hand, radar is more cost-effective. When combined with cameras, radar-camera fusion integrates radar¡¯s depth and velocity estimation with the semantic details provided by cameras. However, current radar feature extractors often use encoders such as PointPillars which were originally designed for LiDAR. Due to radar's sparsity and noise, this can lead to spatial misalignment and difficulties in feature integration.
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IEEE/ICACT20250226 Slide.03
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