1 citations · 1 across the 5 of their papers we have counts for
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High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging
Danial Kamran, Yu Ren, Martin Lauer
Reinforcement learning (RL) has recently been used for solving challenging decision-making problems in the context of automated driving. However, one of the main drawbacks of the p…
Efficient Sampling in POMDPs with Lipschitz Bandits for Motion Planning in Continuous Spaces
Ömer Şahin Taş, Felix Hauser, Martin Lauer
Decision making under uncertainty can be framed as a partially observable Markov decision process (POMDP). Finding exact solutions of POMDPs is generally computationally intractabl…
Fast Lane-Level Intersection Estimation using Markov Chain Monte Carlo Sampling and B-Spline Refinement
Annika Meyer, Jonas Walter, Martin Lauer
Estimating the current scene and understanding the potential maneuvers are essential capabilities of automated vehicles. Most approaches rely heavily on the correctness of maps, bu…
Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme
Piotr Franciszek Orzechowski, Christoph Burger, Martin Lauer
Behavior planning and decision-making are some of the biggest challenges for highly automated systems. A fully automated vehicle (AV) is confronted with numerous tactical and strat…
Capturing Object Detection Uncertainty in Multi-Layer Grid Maps
Sascha Wirges, Marcel Reith-Braun, Martin Lauer +1
We propose a deep convolutional object detector for automated driving applications that also estimates classification, pose and shape uncertainty of each detected object. The input…
LIMO: Lidar-Monocular Visual Odometry
Johannes Graeter, Alexander Wilczynski, Martin Lauer
Higher level functionality in autonomous driving depends strongly on a precise motion estimate of the vehicle. Powerful algorithms have been developed. However, their great majorit…