2 citations · 3 across the 5 of their papers we have counts for
6 papers · 1 filter
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-…
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…
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…