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Multiple Trajectory Prediction with Deep Temporal and Spatial Convolutional Neural Networks
Automated vehicles need to not only perceive their environment, but also predict the possible future behavior of all detected traffic participants in order to safely navigate in complex scenarios and avoid critical situations, ranging from merging on highways to crossing urban intersections.
Long-Term Occupancy Grid Prediction Using Recurrent Neural Networks
We tackle the long-term prediction of scene evolution in a complex downtown scenario for automated driving based on Lidar grid fusion and recurrent neural networks (RNNs).
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