22 citations · 40 across the 8 of their papers we have counts for
8 papers
LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving
Long Nguyen, Micha Fauth, Bernhard Jaeger +4
Simulators can generate virtually unlimited driving data, yet imitation learning policies in simulation still struggle to achieve robust closed-loop performance. Motivated by this…
CaRL: Learning Scalable Planning Policies with Simple Rewards
Bernhard Jaeger, Daniel Dauner, Jens Beißwenger +3
We investigate reinforcement learning (RL) for privileged planning in autonomous driving. State-of-the-art approaches for this task are rule-based, but these methods do not scale t…
Hidden Biases of End-to-End Driving Datasets
Julian Zimmerlin, Jens Beißwenger, Bernhard Jaeger +2
End-to-end driving systems have made rapid progress, but have so far not been applied to the challenging new CARLA Leaderboard 2.0. Further, while there is a large body of literatu…
An Invitation to Deep Reinforcement Learning
Bernhard Jaeger, Andreas Geiger
Training a deep neural network to maximize a target objective has become the standard recipe for successful machine learning over the last decade. These networks can be optimized w…
GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers
Takeru Miyato, Bernhard Jaeger, Max Welling +1
As transformers are equivariant to the permutation of input tokens, encoding the positional information of tokens is necessary for many tasks. However, since existing positional en…
End-to-end Autonomous Driving: Challenges and Frontiers
Li Chen, Penghao Wu, Kashyap Chitta +3
The autonomous driving community has witnessed a rapid growth in approaches that embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle motion pl…
Hidden Biases of End-to-End Driving Models
Bernhard Jaeger, Kashyap Chitta, Andreas Geiger
End-to-end driving systems have recently made rapid progress, in particular on CARLA. Independent of their major contribution, they introduce changes to minor system components. Co…
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
Kashyap Chitta, Aditya Prakash, Bernhard Jaeger +3
How should we integrate representations from complementary sensors for autonomous driving? Geometry-based fusion has shown promise for perception (e.g. object detection, motion for…