Trajectory Prediction

This repository introduces an encoder-decoder sequence to sequence neural network to forecast trajectories. The neural network can be configured to a variable length input and predict a reasonable number of future time steps.

We utilized data from RoboCup SSL games to train the model. The inputs are a sequence of position, velocity and orientation for each time step, like the following:

[x y v_x v_y psi]

The network predicts a sequence of [v_x v_y], which we integrate to find the robot's future trajectory. A representation of the neural network is depicted below:

We also analysed adding information about the ball, in an attempt to improve prediction. We conceived a ball encoder that processes a sequence of position and velocity for the ball and aggregates that into the prediction. A diagram containing the architecture to aggregate information about the ball is available below:

More information

To find out more about the model's architecture, training and testing procedures, please check out my graduation thesis.

Running the models

Prepare the dataset

1. Enter the dataset folder, by doing cd dataset.
2. Run download_dataset.sh. This file downloads the dataset from RoboCup official logs repositories.
3. Run python3 process_dataset.py to process the dataset and prepare it for training and testing.

Train the models

From the root folder, run python3 train_models.py. It will train three models.

• Two models that consume only data about the robots.
• Two models that consume data about tha ball and the robots.
• A multilayer perceptron network.

Each model has been trained in two configurations:

1. A look back window of 30 time steps and a prediction of 15 time steps.
2. A look back window of 60 time steps and a prediction of 30 time steps.

If you would like to visualize plots of batch error and validation error during training, uncomment the plot function in train_models.py and in comparison_tests.py.

Testing the models

Running python3 compare_models.py will run all the trained configurations in a testing set. It will calculate the mean average error, average displacement error and final displacement error and print them. It will also measure such metrics for a Kalman predictor, which serves as a reference for comparison.