54 citations · 74 across the 9 of their papers we have counts for
9 papers
Safety integrity framework for automated driving
Moritz Werling, Rainer Faller, Wolfgang Betz +1
This paper describes the comprehensive safety framework that underpinned the development, release process, and regulatory approval of BMW's first SAE Level 3 Automated Driving Syst…
PlanNetX: Learning an Efficient Neural Network Planner from MPC for Longitudinal Control
Jasper Hoffmann, Diego Fernandez, Julien Brosseit +5
Model predictive control (MPC) is a powerful, optimization-based approach for controlling dynamical systems. However, the computational complexity of online optimization can be pro…
Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning
Branka Mirchevska, Moritz Werling, Joschka Boedecker
Implementing an autonomous vehicle that is able to output feasible, smooth and efficient trajectories is a long-standing challenge. Several approaches have been considered, roughly…
Amortized Q-learning with Model-based Action Proposals for Autonomous Driving on Highways
Branka Mirchevska, Maria Hügle, Gabriel Kalweit +2
Well-established optimization-based methods can guarantee an optimal trajectory for a short optimization horizon, typically no longer than a few seconds. As a result, choosing the…
Deep Surrogate Q-Learning for Autonomous Driving
Maria Kalweit, Gabriel Kalweit, Moritz Werling +1
Challenging problems of deep reinforcement learning systems with regard to the application on real systems are their adaptivity to changing environments and their efficiency w.r.t.…
Deep Inverse Q-learning with Constraints
Gabriel Kalweit, Maria Huegle, Moritz Werling +1
Popular Maximum Entropy Inverse Reinforcement Learning approaches require the computation of expected state visitation frequencies for the optimal policy under an estimate of the r…