Unsupervised Vehicle Trajectory learning in multi-lanes highway
Unlike the original RNN-RBM, this is a model which performs well for real-valued dataset. After training, it can memorize a given vehicle trajectories pattern on a specific route, which can be used to reconstruct vehicle trajectories based on a input dataset polluted by noise.
The structure of RNN-RBM(Nicolas Boulanger-Lewandowski et al.) is illustred below:fig2. structure of RNN_RBM
Put the input trajectories dataset (eg. "trajectories-0750am-0805am.csv" of US Highway 101) in the directory "./ngsim_data/ "
python ngsim_manipulation.py# pretreatement of dataset
python weight_initialization.py# pre-training of RBM Neural networks
python rnn_rbm_train.py <num_epoch># training, num_epoch can be about 10
python rnn_rbm_reconstruction.py ./parameter_checkpoints/<fichier .ckpt># reconstruction of trajectories
The output picture is stocked in "./picture_folder".
There are different versions of ngsim_manipulation, as well as for rnn_rbm, RBM, draw, they deal with different situations. For using them, rename them(delete the version notation).
Further work can be conducted, for example, changing the standard RNN by LSTM, or changing RBM by DBN, etc.