2 citations · 2 across the 5 of their papers we have counts for
6 papers
TriO: Tri-Modal Unsupervised Occupancy World Model for Anything Perception
Quinlan Sykora, Sourav Biswas, Christopher Diehl +3
We present TriO, a multi-modal unsupervised world model that predicts 4D occupancy, obstacle segmentation, flow and LiDAR. In contrast to prior work, TriO utilizes three distinct s…
HiLO: High-Level Object Fusion for Autonomous Driving using Transformers
Timo Osterburg, Franz Albers, Christopher Diehl +2
The fusion of sensor data is essential for a robust perception of the environment in autonomous driving. Learning-based fusion approaches mainly use feature-level fusion to achieve…
LoRD: Adapting Differentiable Driving Policies to Distribution Shifts
Christopher Diehl, Peter Karkus, Sushant Veer +2
Distribution shifts between operational domains can severely affect the performance of learned models in self-driving vehicles (SDVs). While this is a well-established problem, pri…
Energy-based Potential Games for Joint Motion Forecasting and Control
Christopher Diehl, Tobias Klosek, Martin Krüger +3
This work uses game theory as a mathematical framework to address interaction modeling in multi-agent motion forecasting and control. Despite its interpretability, applying game th…
On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions
Christopher Diehl, Tobias Klosek, Martin Krüger +2
Game theory offers an interpretable mathematical framework for modeling multi-agent interactions. However, its applicability in real-world robotics applications is hindered by seve…
Differentiable Constrained Imitation Learning for Robot Motion Planning and Control
Christopher Diehl, Janis Adamek, Martin Krüger +2
Motion planning and control are crucial components of robotics applications like automated driving. Here, spatio-temporal hard constraints like system dynamics and safety boundarie…