4 citations · 7 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
Closing the Navigation Compliance Gap in End-to-end Autonomous Driving
Hanfeng Wu, Marlon Steiner, Michael Schmidt +2
Trajectory-scoring planners achieve high navigation compliance when following the expert's original command, yet they struggle at intersections when presented with alternative comm…
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…
RetroMotion: Retrocausal Motion Forecasting Models are Instructable
Royden Wagner, Omer Sahin Tas, Felix Hauser +7
Motion forecasts of road users (i.e., agents) vary in complexity depending on the number of agents, scene constraints, and interactions. In particular, the output space of joint tr…