The Explainability-aware Frustum Attack (EFA) guided by Saliency-LiDAR (SALL) maps reduces detection recall by more than 15 percentage points using 25-50% fewer perturbed frustums than non-saliency baselines on KITTI and nuScenes for detectors like PointPillars and SECOND.
Sensors18(10), 3337 (2018)
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
CooperScene provides 59K synchronized frames with 344K 3D annotations from multi-modal sensors on 3 CAVs and 1 RSU plus real C-V2X communication traces for cooperative autonomy benchmarking.
citing papers explorer
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Explainability-Aware Frustum Attack: Exposing Structural Vulnerabilities in LiDAR-Based 3D Object Detectors
The Explainability-aware Frustum Attack (EFA) guided by Saliency-LiDAR (SALL) maps reduces detection recall by more than 15 percentage points using 25-50% fewer perturbed frustums than non-saliency baselines on KITTI and nuScenes for detectors like PointPillars and SECOND.
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CooperScene: Multi-Modal Cooperative Autonomy Benchmark with C-V2X Communication Characterization
CooperScene provides 59K synchronized frames with 344K 3D annotations from multi-modal sensors on 3 CAVs and 1 RSU plus real C-V2X communication traces for cooperative autonomy benchmarking.