In a series of experiments, we confirm the soundness of our metric by applying it in controllable task setups and on unseen data.
Scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous situations, termed corner cases (CC).
Scalable systems for automated driving have to reliably cope with an open-world setting.
Further, we provide theoretical and empirical analyses regarding the implications of model-usage on constrained policy optimization problems and introduce a practical algorithm that accelerates policy search with model-generated data.
For reliable environment perception, the use of temporal information is essential in some situations.
Data driven approaches for decision making applied to automated driving require appropriate generalization strategies, to ensure applicability to the world's variability.
Autonomous vehicles need to have a semantic understanding of the three-dimensional world around them in order to reason about their environment.
Monte Carlo Tree Search (MCTS) has proven to be capable of solving challenging tasks in domains such as Go, chess and Atari.
Humans are well equipped with the capability to predict the actions of multiple interacting traffic participants and plan accordingly, without the need to directly communicate with others.
This paper presents a novel CNN-based approach for synthesizing high-resolution LiDAR point cloud data.
With automated focal loss we introduce a new loss function which substitutes this hyperparameter by a parameter that is automatically adapted during the training progress and controls the amount of focusing on hard training examples.
This paper introduces therefore a data-driven approach utilizing a deep convolutional neural network (CNN): Given the current driving situation, future ego-vehicle poses can be directly generated from the output of the CNN allowing to guide the motion planner efficiently towards the optimal solution.
Urban traffic scenarios often require a high degree of cooperation between traffic participants to ensure safety and efficiency.
This ensures a consistent model of the environment across scenarios as well as a behavior adaptation function, enabling on-line changes of desired behaviors without re-training.
This work presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative planning using macro-actions for automated vehicles in heterogeneous environments.
Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical applications.