11 papers
XD-RCDepth: Lightweight Radar-Camera Depth Estimation with Explainability-Aligned and Distribution-Aware Distillation
Huawei Sun, Zixu Wang, Xiangyuan Peng +4
Depth estimation remains central to autonomous driving, and radar-camera fusion offers robustness in adverse conditions by providing complementary geometric cues. In this paper, we…
GenFacts-Generative Counterfactual Explanations for Multi-Variate Time Series
Sarah Seifi, Anass Ibrahimi, Tobias Sukianto +3
Counterfactual explanations aim to enhance model transparency by showing how inputs can be minimally altered to change predictions. For multivariate time series, existing methods o…
Feature Identification for Hierarchical Contrastive Learning
Julius Ott, Nastassia Vysotskaya, Huawei Sun +2
Hierarchical classification is a crucial task in many applications, where objects are organized into multiple levels of categories. However, conventional classification approaches…
TRIDE: A Text-assisted Radar-Image weather-aware fusion network for Depth Estimation
Huawei Sun, Zixu Wang, Hao Feng +3
Depth estimation, essential for autonomous driving, seeks to interpret the 3D environment surrounding vehicles. The development of radar sensors, known for their cost-efficiency an…
4D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challenges
Xiangyuan Peng, Miao Tang, Huawei Sun +3
Intelligent transportation systems require accurate and reliable sensing. However, adverse environments, such as rain, snow, and fog, can significantly degrade the performance of L…
ELMAR: Enhancing LiDAR Detection with 4D Radar Motion Awareness and Cross-modal Uncertainty
Xiangyuan Peng, Miao Tang, Huawei Sun +3
LiDAR and 4D radar are widely used in autonomous driving and robotics. While LiDAR provides rich spatial information, 4D radar offers velocity measurement and remains robust under…