Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning
A key challenge for autonomous driving is safe trajectory planning in cluttered, urban environments with dynamic obstacles, such as pedestrians, bicyclists, and other vehicles. A reliable prediction of the future environment, including the behavior of dynamic agents, would allow planning algorithms to proactively generate a trajectory in response to a rapidly changing environment... (read more)
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Methods used in the Paper
| METHOD | TYPE | |
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Memory Network
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Working Memory Models |