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Found 5 papers, 1 papers with code
Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions
Reinforcement Learning is a highly active research field with promising advancements.
Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving
We provide a characterization of world models and relate individual components to previous works in anomaly detection to facilitate further research in the field.
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.
Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules
Driving on roads is restricted by various traffic rules, aiming to ensure safety for all traffic participants.
Cycle-Consistent World Models for Domain Independent Latent Imagination
Our experiments using different modalities in the CARLA simulator showed that this enables CCWM to outperform state-of-the-art domain adaptation approaches.
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