Problem
Research questions and friction points this paper is trying to address.
asymmetric sensor degradation
end-to-end driving
multimodal fusion
reliability estimation
Variance-Guided Spatial Attention Fusion for Robust End-to-End Driving under Asymmetric Sensor Degradation
🤖 AI Summary
本文提出了一种基于方差引导的空间注意力融合方法(VG-SAF)，以解决在非对称传感器退化情况下的端到端驾驶问题，通过模拟故障、预测可靠性并使用混合注意力机制来提高驾驶鲁棒性。
📝 Abstract
End-to-end multimodal driving has progressed rapidly by fusing camera and LiDAR streams. Existing pipelines remain fragile under asymmetric sensor degradation, where either an entire modality or only a localized region is corrupted while other regions remain useful. The key difficulty is not simply to add an uncertainty head, but to obtain dense reliability supervision, calibrate this reliability against physical fault severity, and use it before unreliable features bias the planner. We propose Variance-Guided Spatial Attention Fusion (VG-SAF), in which dense heteroscedastic reliability estimates act as interpretable spatial gates. The framework couples three components. First, a physically grounded augmentor simulates representative camera and LiDAR failures and emits a continuous spatial mask, providing dense supervision without additional annotation. Second, modality-specific experts predict per-pixel reliability scales through cross-branch dense distillation in log space, enforcing a monotone severity-to-scale response. Third, calibrated reliability maps drive a hybrid attention mechanism that suppresses unreliable cells with a local spatial gate and arbitrates between modalities through a cross-modal trust softmax. A Laplace uncertainty head emits a systemic waypoint uncertainty scale that signals severe or combined sensor degradation, including severities outside the training ranges. On the CARLA Longest6 benchmark, VG-SAF consistently improves closed-loop robustness over the baselines across camera-only, LiDAR-only, and joint degradation regimes, as measured by driving score, route completion, and infraction score.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Variance-Guided Spatial Attention Fusion
asymmetric sensor degradation
dense reliability supervision
hybrid attention mechanism
Laplace uncertainty head
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