activity
20232026
most citedDriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch

14 citations · 29 across the 6 of their papers we have counts for

collaborators

6 papers

cs.RO2026

Chameleon: Control-Indexed Prospective Memory for Visuomotor Manipulation

Xinying Guo, Chenxi Jiang, Hyun Bin Kim +4

Robots often observe information that determines a future action long before that action is executed. In a shell game, for example, a robot first sees which cup hides the ball, wat…

cs.RO2024★ 2 cited

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…

cs.RO2024

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…

cs.RO2024★ 9 cited

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…

cs.RO2023★ 14 cited

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…

cs.RO2023★ 4 cited

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…