activity
20222026
most citedSafety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer

55 citations · 70 across the 17 of their papers we have counts for

collaborators
Showing 2026Show all

8 papers · 1 filter

cs.RO2026

BeyondSight: Object Permanence for End-to-End Autonomous Driving

Sandro Papais, Letian Wang, Mudit Jain +2

Autonomous driving operates in partially observable environments where actors may become fully occluded by other vehicles or infrastructure. Most end-to-end driving systems implici…

cs.CV2026

Video Generation Models are General-Purpose Vision Learners

Letian Wang, Chuhan Zhang, Rishabh Kabra +9

Driven by next-token prediction, NLP shifted from task-specific models into powerful generalist foundation models. What, then, is the equivalent catalyst needed to achieve a genera…

cs.RO2026

Grounded World Model: Latent Planning with Language Goals

Quanyi Li, Lan Feng, Haonan Zhang +4

World models such as DINO-WM and LeWM specify the goal with an image, which is difficult to obtain in advance for novel tasks. We present the Grounded World Model (GWM), a latent w…

cs.CV2026

LMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving

Hao Shao, Letian Wang, Yang Zhou +5

Recent years have seen remarkable progress in autonomous driving, yet generalization to long-tail and open-world scenarios remains a major bottleneck for large-scale deployment. To…

cs.CV2026

DriveDreamer-Policy: A Geometry-Grounded World-Action Model for Unified Generation and Planning

Yang Zhou, Xiaofeng Wang, Hao Shao +8

Recently, world-action models (WAM) have emerged to bridge vision-language-action (VLA) models and world models, unifying their reasoning and instruction-following capabilities and…

q-bio.QM2026

Spectral Coherence Index: A Model-Free Metric for Protein Structural Ensemble Quality Assessment

Yuda Bi, Huaiwen Zhang, Jingnan Sun +1

Protein structural ensembles from NMR spectroscopy capture biologically important conformational heterogeneity, but it remains difficult to determine whether observed variation ref…

cs.CV2026

SCATR: Mitigating New Instance Suppression in LiDAR-based Tracking-by-Attention via Second Chance Assignment and Track Query Dropout

Brian Cheong, Letian Wang, Sandro Papais +1

LiDAR-based tracking-by-attention (TBA) frameworks inherently suffer from high false negative errors, leading to a significant performance gap compared to traditional LiDAR-based t…

cs.CV2026

DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving

Yang Zhou, Hao Shao, Letian Wang +3

Video generation models, as one form of world models, have emerged as one of the most exciting frontiers in AI, promising agents the ability to imagine the future by modeling the t…