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Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based Recurrent Neural Networks
To plan safe and comfortable trajectories for automated vehicles on highways, accurate predictions of traffic situations are needed.
A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions
Already today, driver assistance systems help to make daily traffic more comfortable and safer.
Driver Assistance for Safe and Comfortable On-Ramp Merging Using Environment Models Extended through V2X Communication and Role-Based Behavior Predictions
To overcome these challenges, an extended environment model is a reasonable choice.
Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior
More precisely, we investigate how a state-of-the-art approach for lateral motion prediction is influenced by one selected external condition, namely the traffic density.
Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Sets
By observing their environment as well as other traffic participants, humans are enabled to drive road vehicles safely.
