4 citations · 7 across the 11 of their papers we have counts for
4 papers · 1 filter
Mosaic: An Extensible Framework for Composing Rule-Based and Learned Motion Planners
Nick Le Large, Marlon Steiner, Lingguang Wang +4
Safe and explainable motion planning remains a central challenge in autonomous driving. While rule-based planners offer predictable and explainable behavior, they often fail to gra…
Safety Reinforced Model Predictive Control (SRMPC): Improving MPC with Reinforcement Learning for Motion Planning in Autonomous Driving
Johannes Fischer, Marlon Steiner, Ömer Sahin Tas +1
Model predictive control (MPC) is widely used for motion planning, particularly in autonomous driving. Real-time capability of the planner requires utilizing convex approximation o…
Prediction-Driven Motion Planning: Route Integration Strategies in Attention-Based Prediction Models
Marlon Steiner, Royden Wagner, Ömer Sahin Tas +1
Combining motion prediction and motion planning offers a promising framework for enhancing interactions between automated vehicles and other traffic participants. However, this int…
FlowDrive: moderated flow matching with data balancing for trajectory planning
Lingguang Wang, Ömer Şahin Taş, Marlon Steiner +1
Learning-based planners are sensitive to the long-tailed distribution of driving data. Common maneuvers dominate datasets, while dangerous or rare scenarios are sparse. This imbala…