158 citations · 419 across the 62 of their papers we have counts for
33 papers · 1 filter
Semantic-Aware, Physics-Informed, Geometry-Grounded Weather Video Synthesis
Chenghao Qian, Nedko Savov, Lingdong Kong +7
Weather synthesis aims to add weather effects to input videos while preserving scene identity, structure, and motion. The key limitation of existing methods is the lack of diversit…
nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving
Zhiyu Huang, Johnson Liu, Rui Song +13
Reasoning is essential for autonomous driving (AD) in long-tail scenarios, where vehicles must apply commonsense knowledge, understand spatial relations, infer agent interactions,…
What-If World: A Causal Benchmark for General World Models in Embodied Scenarios
Kunlin Cai, Rui Song, Jinghuai Zhang +7
Video generation models are increasingly used as world simulators for tasks like driving and robotic manipulation. What matters in these settings is not whether a single video look…
ConFixGS: Learning to Fix Feedforward 3D Gaussian Splatting with Confidence-Aware Diffusion Priors in Driving Scenes
Rui Song, Tianhui Cai, Markus Gross +5
Feedforward 3D Gaussian Splatting (3DGS) often struggles in trajectory-based sparse-view driving scenes. Existing Gaussian repair methods mainly target optimization-based 3DGS, whi…
EnerGS: Energy-Based Gaussian Splatting with Partial Geometric Priors
Rui Song, Tianhui Cai, Markus Gross +5
3D Gaussian Splatting (3DGS) has been widely adopted for scene reconstruction, where training inherently constitutes a highly coupled and non-convex optimization problem. Recent wo…
SpanVLA: Learning from Negative-Recovery Samples with Fast Action Bridging for Vision-Language-Action Model
Zewei Zhou, Ruining Yang, Xuewei Qi +9
Vision-Language-Action (VLA) models bring rich world knowledge and reasoning capabilities to autonomous driving. However, most driving VLAs are trained only on positive expert demo…
MIC-BEV: Multi-Infrastructure Camera Bird's-Eye-View Transformer with Relation-Aware Fusion for 3D Object Detection
Yun Zhang, Zhaoliang Zheng, Johnson Liu +5
Infrastructure-based perception plays a crucial role in intelligent transportation systems, offering global situational awareness and enabling cooperative autonomy. However, existi…
QuantV2X: A Fully Quantized Multi-Agent System for Cooperative Perception
Seth Z. Zhao, Huizhi Zhang, Zhaowei Li +11
Cooperative perception through Vehicle-to-Everything (V2X) communication offers significant potential for enhancing vehicle perception by mitigating occlusions and expanding the fi…
TurboTrain: Towards Efficient and Balanced Multi-Task Learning for Multi-Agent Perception and Prediction
Zewei Zhou, Seth Z. Zhao, Tianhui Cai +3
End-to-end training of multi-agent systems offers significant advantages in improving multi-task performance. However, training such models remains challenging and requires extensi…
RelMap: Enhancing Online Map Construction with Class-Aware Spatial Relation and Semantic Priors
Tianhui Cai, Yun Zhang, Zewei Zhou +2
Online high-definition (HD) map construction is crucial for scaling autonomous driving systems. While Transformer-based methods have become prevalent in online HD map construction,…
AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning
Zewei Zhou, Tianhui Cai, Seth Z. Zhao +4
Recent advancements in Vision-Language-Action (VLA) models have shown promise for end-to-end autonomous driving by leveraging world knowledge and reasoning capabilities. However, c…
V2X-ReaLO: An Open Online Framework and Dataset for Cooperative Perception in Reality
Hao Xiang, Zhaoliang Zheng, Xin Xia +6
Cooperative perception enabled by Vehicle-to-Everything (V2X) communication holds significant promise for enhancing the perception capabilities of autonomous vehicles, allowing the…
AgentAlign: Misalignment-Adapted Multi-Agent Perception for Resilient Inter-Agent Sensor Correlations
Zonglin Meng, Yun Zhang, Zhaoliang Zheng +2
Cooperative perception has attracted wide attention given its capability to leverage shared information across connected automated vehicles (CAVs) and smart infrastructures to addr…
V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction
Zewei Zhou, Hao Xiang, Zhaoliang Zheng +11
Vehicle-to-everything (V2X) technologies offer a promising paradigm to mitigate the limitations of constrained observability in single-vehicle systems. Prior work primarily focuses…
Multi-scale Temporal Fusion Transformer for Incomplete Vehicle Trajectory Prediction
Zhanwen Liu, Chao Li, Yang Wang +4
Motion prediction plays an essential role in autonomous driving systems, enabling autonomous vehicles to achieve more accurate local-path planning and driving decisions based on pr…
CooPre: Cooperative Pretraining for V2X Cooperative Perception
Seth Z. Zhao, Hao Xiang, Chenfeng Xu +3
Existing Vehicle-to-Everything (V2X) cooperative perception methods rely on accurate multi-agent 3D annotations. Nevertheless, it is time-consuming and expensive to collect and ann…
MSTF: Multiscale Transformer for Incomplete Trajectory Prediction
Zhanwen Liu, Chao Li, Nan Yang +4
Motion forecasting plays a pivotal role in autonomous driving systems, enabling vehicles to execute collision warnings and rational local-path planning based on predictions of the…
V2X-Real: a Large-Scale Dataset for Vehicle-to-Everything Cooperative Perception
Hao Xiang, Zhaoliang Zheng, Xin Xia +15
Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled autonomous vehicles to share sensing information to see through occlusions, greatly boosting the percep…
Breaking Data Silos: Cross-Domain Learning for Multi-Agent Perception from Independent Private Sources
Jinlong Li, Baolu Li, Xinyu Liu +3
The diverse agents in multi-agent perception systems may be from different companies. Each company might use the identical classic neural network architecture based encoder for fea…
Optimizing the Placement of Roadside LiDARs for Autonomous Driving
Wentao Jiang, Hao Xiang, Xinyu Cai +5
Multi-agent cooperative perception is an increasingly popular topic in the field of autonomous driving, where roadside LiDARs play an essential role. However, how to optimize the p…
Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception
Si Liu, Chen Gao, Yuan Chen +8
Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) a…
Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather
Jinlong Li, Runsheng Xu, Xinyu Liu +5
Typically, object detection methods for autonomous driving that rely on supervised learning make the assumption of a consistent feature distribution between the training and testin…
S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality
Jinlong Li, Runsheng Xu, Xinyu Liu +4
Due to the lack of enough real multi-agent data and time-consuming of labeling, existing multi-agent cooperative perception algorithms usually select the simulated sensor data for…
HM-ViT: Hetero-modal Vehicle-to-Vehicle Cooperative perception with vision transformer
Hao Xiang, Runsheng Xu, Jiaqi Ma
Vehicle-to-Vehicle technologies have enabled autonomous vehicles to share information to see through occlusions, greatly enhancing perception performance. Nevertheless, existing wo…