10 papers
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…
Toward Efficient and Robust Behavior Models for Multi-Agent Driving Simulation
Fabian Konstantinidis, Moritz Sackmann, Ulrich Hofmann +1
Scalable multi-agent driving simulation requires behavior models that are both realistic and computationally efficient. We address this by optimizing the behavior model that contro…
LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset
Royden Wagner, Omer Sahin Tas, Jaime Villa +18
In real-world domains such as self-driving, generalization to rare scenarios remains a fundamental challenge. To address this, we introduce a new dataset designed for end-to-end dr…
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…
Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous Driving
Yinzhe Shen, Omer Sahin Tas, Kaiwen Wang +2
Perceiving the environment and its changes over time corresponds to two fundamental yet heterogeneous types of information: semantics and motion. Previous end-to-end autonomous dri…
From Marginal to Joint Predictions: Evaluating Scene-Consistent Trajectory Prediction Approaches for Automated Driving
Fabian Konstantinidis, Ariel Dallari Guerreiro, Raphael Trumpp +4
Accurate motion prediction of surrounding traffic participants is crucial for the safe and efficient operation of automated vehicles in dynamic environments. Marginal prediction mo…