From the 1 of 11 linked papers with an AI index.
4 papers · 1 filter
Mosaic: An Extensible Framework for Composing Rule-Based and Learned Motion Planners
Nick Le Large, Marlon Steiner, Lingguang Wang +4
Mosaic is a framework that combines rule‑based and learned motion planners using arbitration graphs, separating trajectory verification from selection to improve safety and perform…
FlowDrive: moderated flow matching with data balancing for trajectory planning
Lingguang Wang, Ãmer Åahin TaÅ, 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…
Safety Reinforced Model Predictive Control (SRMPC): Improving MPC with Reinforcement Learning for Motion Planning in Autonomous Driving
Johannes Fischer, Marlon Steiner, Ãmer 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, Ãmer 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…