![]() Handcrafted approaches usually rely on a model for human motion prediction. The distinction can be made between the handcrafted approaches and the learned approaches. OGM prediction is the line of study our work matches most closely. In our work, we depart from these object-centric methods and provide spatiotemporal occupancy predictions. ![]() However, they all are dependent on the detection algorithm results and do not easily incorporate multi-modal predictions. Similar object tracking and trajectory prediction techniques are also used in the context of autonomous driving. detect individual obstacles and predicts their speeds to avoid “freezing zones”. In both cases, they rely on a detection and tracking algorithm. Įxploit a Hidden Markov Model to predict future states from a history of observations. use an LSTM-based network to predict people trajectories and plan around them, while Peddi et al. Following the success of recurrent neural network (RNN) and in particular long short-term memory networks (LSTM) for trajectory prediction, the idea of isolating each obstacle as a distinct object has received a lot of attention. Is a very common approach to enable motion planning in environments with dynamic obstacles. Object tracking and trajectory prediction
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