3 citations · 6 across the 15 of their papers we have counts for
11 papers · 1 filter
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,…
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…
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…
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…
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…