14 citations · 30 across the 11 of their papers we have counts for
12 papers
HODAgent: Towards On-Demand, Responsive Humanoids for Physical World Human Interaction
Wang Warren Chen, Jiahao Zhang, Zhenjiang Li +6
We propose HODAgent, a System-2 embodied agent for humanoid robots in service settings, addressing situated intent, responsive execution, task revision, and outcome verification. I…
RMGS-SLAM: Real-time Multi-sensor Gaussian Splatting SLAM
Dongen Li, Yi Liu, Junqi Liu +8
Achieving real-time Simultaneous Localization and Mapping (SLAM) based on 3D Gaussian splatting (3DGS) in large-scale real-world environments remains challenging, as existing metho…
PIE: Perception and Interaction Enhanced End-to-End Motion Planning for Autonomous Driving
Chengran Yuan, Zijian Lu, Zhanqi Zhang +8
End-to-end motion planning is promising for simplifying complex autonomous driving pipelines. However, challenges such as scene understanding and effective prediction for decision-…
AGI-Elo: How Far Are We From Mastering A Task?
Shuo Sun, Yimin Zhao, Christina Dao Wen Lee +8
As the field progresses toward Artificial General Intelligence (AGI), there is a pressing need for more comprehensive and insightful evaluation frameworks that go beyond aggregate…
IMPACT: Behavioral Intention-aware Multimodal Trajectory Prediction with Adaptive Context Trimming
Jiawei Sun, Xibin Yue, Jiahui Li +6
While most prior research has focused on improving the precision of multimodal trajectory predictions, the explicit modeling of multimodal behavioral intentions (e.g., yielding, ov…
RMP-YOLO: A Robust Motion Predictor for Partially Observable Scenarios even if You Only Look Once
Jiawei Sun, Jiahui Li, Tingchen Liu +6
We introduce RMP-YOLO, a unified framework designed to provide robust motion predictions even with incomplete input data. Our key insight stems from the observation that complete a…
DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba
Chengran Yuan, Zhanqi Zhang, Jiawei Sun +8
Motion planning is a challenging task to generate safe and feasible trajectories in highly dynamic and complex environments, forming a core capability for autonomous vehicles. In t…
ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction under Uncertainties
Jiahui Li, Tianle Shen, Zekai Gu +5
Motion prediction is a challenging problem in autonomous driving as it demands the system to comprehend stochastic dynamics and the multi-modal nature of real-world agent interacti…
ControlMTR: Control-Guided Motion Transformer with Scene-Compliant Intention Points for Feasible Motion Prediction
Jiawei Sun, Chengran Yuan, Shuo Sun +7
The ability to accurately predict feasible multimodal future trajectories of surrounding traffic participants is crucial for behavior planning in autonomous vehicles. The Motion Tr…
DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch
Shuo Sun, Zekai Gu, Tianchen Sun +5
Realistic and diverse traffic scenarios in large quantities are crucial for the development and validation of autonomous driving systems. However, owing to numerous difficulties in…
CARLA-Loc: Synthetic SLAM Dataset with Full-stack Sensor Setup in Challenging Weather and Dynamic Environments
Yuhang Han, Zhengtao Liu, Shuo Sun +4
The robustness of SLAM (Simultaneous Localization and Mapping) algorithms under challenging environmental conditions is critical for the success of autonomous driving. However, the…
GET-DIPP: Graph-Embedded Transformer for Differentiable Integrated Prediction and Planning
Jiawei Sun, Chengran Yuan, Shuo Sun +5
Accurately predicting interactive road agents' future trajectories and planning a socially compliant and human-like trajectory accordingly are important for autonomous vehicles. In…