15 citations · 18 across the 26 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…
Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition
Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli +2
Rule-based models offer interpretability but struggle with complex data, while deep neural networks excel in performance yet lack transparency. This work investigates a neuro-symbo…
MoRAL: Motion-aware Multi-Frame 4D Radar and LiDAR Fusion for Robust 3D Object Detection
Xiangyuan Peng, Yu Wang, Miao Tang +3
Reliable autonomous driving systems require accurate detection of traffic participants. To this end, multi-modal fusion has emerged as an effective strategy. In particular, 4D rada…
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…
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition
Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli +2
The EU AI Act underscores the importance of transparency, user-centricity, and robustness in AI systems, particularly for high-risk systems. In response, we present advancements in…
MutualForce: Mutual-Aware Enhancement for 4D Radar-LiDAR 3D Object Detection
Xiangyuan Peng, Huawei Sun, Kay Bierzynski +3
Radar and LiDAR have been widely used in autonomous driving as LiDAR provides rich structure information, and radar demonstrates high robustness under adverse weather. Recent studi…