1 citations · 1 across the 7 of their papers we have counts for
7 papers
DVMap: Fine-Grained Pluralistic Value Alignment via High-Consensus Demographic-Value Mapping
Pengyun Zhu, Yuqi Ren, Zhen Wang +2
Current Large Language Models (LLMs) typically rely on coarse-grained national labels for pluralistic value alignment. However, such macro-level supervision often obscures intra-co…
SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
Haiwen Diao, Penghao Wu, Hanming Deng +55
Recent large vision-language models (VLMs) remain fundamentally constrained by a persistent dichotomy: understanding and generation are treated as distinct problems, leading to fra…
DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving
Lingjun Zhang, Changjie Wu, Linzhe Shi +6
End-to-end autonomous driving systems are increasingly integrating Vision-Language Model (VLM) architectures, incorporating text reasoning or visual reasoning to enhance the robust…
DriveFuture: Future-Aware Latent World Models for Autonomous Driving
Yufeng Hong, Xiaotian Zhou, Yingyan Li +6
Existing latent world models for autonomous driving have opened a promising path toward future-aware driving intelligence. However, they typically treat future latent states as pre…
Can Attribution Predict Risk? From Multi-View Attribution to Planning Risk Signals in End-to-End Autonomous Driving
Le Yang, Haijun Liu, Jiawei Liang +2
End-to-end autonomous driving models generate future trajectories from multi-view inputs, improving system integration but introducing opaque decisions and hard-to-localize risks.…
Progressive Bird's Eye View Perception for Safety-Critical Autonomous Driving: A Comprehensive Survey
Yan Gong, Naibang Wang, Jianli Lu +13
Bird's-Eye-View (BEV) perception has become a foundational paradigm in autonomous driving, enabling unified spatial representations that support robust multi-sensor fusion and mult…
Stable at Any Speed: Speed-Driven Multi-Object Tracking with Learnable Kalman Filtering
Yan Gong, Mengjun Chen, Hao Liu +5
Multi-object tracking (MOT) enables autonomous vehicles to continuously perceive dynamic objects, supplying essential temporal cues for prediction, behavior understanding, and safe…