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- W4207059782 abstract "The prediction of surrounding vehicles' trajectories based on the ego vehicle's perception is crucial for safe and predictive planning of an autonomous vehicle. In particular on highways, a late detection of cutting in vehicles causes uncomfortable or even hazardous braking maneuvers. To address this, we propose a joint trajectory and cut-in prediction on highways for real-world application. For a large data set with diverse cut-in scenarios, we leverage automatic labeling functions and a retrospective view on recorded driving data of a test vehicle fleet. This allows us to extract 1856 cut-in, an equal number of passing maneuvers, and to generate the trajectory targets and cut-in labels for each time step automatically. For a proper evaluation, we review and correct the automatically generated cut-in labels of the validation and test set. Given this data set, we apply an LSTM-based encoder-decoder model and train a bimodal trajectory prediction with a passing and a cut-in mode. To improve mode separation, we evaluate the effectiveness of soft and hard output constraints. We implement soft constraints with an additional loss term and the hard output constraints according to the recently proposed neural network architecture ConstraintNet. Both constraint types improve mode separation while the hard constraints achieve additionally the best overall performance with respect to mode separation and trajectory accuracy. Further, we show that the duration of cutin labels and an additional weighting of the lateral part of the trajectory loss is important for well-separated modes." @default.
- W4207059782 created "2022-01-26" @default.
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- W4207059782 date "2021-12-05" @default.
- W4207059782 modified "2023-10-01" @default.
- W4207059782 title "Joint Vehicle Trajectory and Cut-In Prediction on Highways using Output Constrained Neural Networks" @default.
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- W4207059782 doi "https://doi.org/10.1109/ssci50451.2021.9659971" @default.
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