arXiv:2610.35110v1 Announce Type: new Abstract: A cooperative 3D detector can miss a vehicle even when the LiDAR points on it remain unchanged. We study this failure under adverse weather as Weather-...
The paper investigates how snow affects LiDAR-based object detection, finding that detection accuracy actually improves with higher snow intensity. This counter‑intuitive result is explained by an increase in object point count due to snow accumulation, which outweighs the noise introduced by falling snow. The authors argue that broad weather severity bins can mislead robustness assessments unless atmospheric noise and surface accumulation effects are disentangled.
arXiv:2607. 23537v1 Announce Type: new Abstract: Autonomous driving under adverse weather remains a critical challenge, yet existing vision-language benchmarks mainly evaluate under standard conditions, synthetic corruptions, or single modality.
The paper introduces Grace-BEV, a lightweight framework that enhances the robustness of Lift‑Splat‑Shoot (LSS) based bird’s‑eye view (BEV) perception models against sensor corruptions. Grace‑BEV operates in aligned BEV space, combining a LiDAR‑guided image‑BEV expert with a LiDAR‑independent visual expert, and uses a TrustGate Router and FailSafe Fusion Block to mix and recalibrate their outputs. Experiments on nuScenes-R and nuScenes-C show that Grace‑BEV maintains strong performance under diverse corruptions, recovers up to 34.7 mAP when LiDAR is lost, and even improves clean accuracy by up to 1.4 mAP with only 1.2M additional parameters.
3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this ``long-range'' scenario is routine in traffic.
The paper presents a foundation-guided auto‑annotation pipeline that improves standard autonomous driving object detectors in adverse weather. By benchmarking YOLOv8, Co‑DETR, and SAM3 on a custom dataset of 25 operational scenarios, the authors find SAM3 to be the most robust and use it offline to generate pseudo‑labels. Fine‑tuning YOLOv8 on these labels boosts overall mAP by 16.04% and yields significant gains in specific conditions such as Residential Direct Sunlight (32.73%) and Highway Fog (28.65%).
Standard deployment-ready object detectors for autonomous vehicles degrade in adverse weather and lighting conditions without being trained on extensive domain-specific data. While large-scale vision...
arXiv:2606. 09634v1 Announce Type: cross Abstract: 3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications.
Long-range 3D object detection remains difficult for autonomous vehicles because LiDAR returns become sparse and camera evidence weakens with distance, while automotive radar keeps returning measureme...
arXiv:2609.14619v1 Announce Type: new Abstract: LiDAR--4-D radar fusion combines accurate spatial geometry with motion and reflectivity cues from radar, offering a promising solution for 3-D object d...
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...
arXiv:2610.00141v1 Announce Type: new Abstract: Vision-based anti-UAV systems must function in poor visibility, yet most benchmarks use only clear-sky footage, and previous robustness studies treat a...