This repository contains the code for our paper titled "Polar Collision Grids: Effective Interaction Modelling for Pedestrian Trajectory Prediction in Shared Space Using Collision Checks", which was published and presented at ITSC 2023. The complete paper is accessible via the IEEE portal or ArXiv.
Create a virtual environmnet using python version 3.9, and install the required python packages
pip install -r requirements.txtInstall pytorch version 2.2.1 using the instructions here
This repository contains the code for our Polar Collision Grid model (model_collisionGrid.py), along with the baseline models used for comparison in our paper. These baselines consist of the Social LSTM model (with relevant python files in model_SocialLSTM.py and grid.py), the Vanilla LSTM model (model_Vanilla_LSTM.py), and the Linear Regression model (LinearRegression.py). The selection of the method for training and testing is determined by the method argument in both train.py and test.py.
The HBS dataset, which includes trajectories of both pedestrians and vehicles collected from a shared space, is used for training and testing our data-driven trajectory prediction model. The initial dataset, stored as hbs.csv in the Data folder, undergoes preprocessing. This involves adapting it to a format compatible with our code, utilizing the functions available in the datalader.py file. Subsequently, the data is partitioned into train and test sets, which are already stored in the Data folder.
Our Polar Collision Grid model, as well as any of the data-driven baseline models (Social LSTM and Vanilla LSTM), can be trained for 200 epochs by executing the train.py file. A log file containing progress information during training will be stored under Store_Results\log. Following the completion of each epoch, the model will be saved in the Store_Results\model directory. Additionally, a plot illustrating the average displacement error and Negative Log Likelihood loss over epochs, and a more detailed one over batch numbers, will be saved in the Store_Results\plot\train\ directory.
Our trained models are stored in folders named relative to their corresponding models under the Store_Results\model directory. By executing the test.py script, the model saved in these directories will be loaded and tested on the test set. Depending on the chosen method, the epoch argument associated with the saved model in its folder should be adjusted. The terminal will display the performance of the saved model for the defined evaluation metrics, and the outputted trajectories for the test set will be saved as a test_result.pkl file in the Store_Results/plot/test directory.
For the results reported in the paper, the test files are stored in the method's associated folder within the Store_Results/plot/test directory. These files can be utilized to run the visualization.py script and generate the table results in the paper by executing the TableResults.py file for the selected method.
The predicted trajectories can be visualized for selected samples in the test set by running the visualization.py file. The resulting figures are stored in the Store_Results\plot\test\plt\compare directory.
If you find this code useful in your research, please consider citing our paper:
@inproceedings{golchoubian2023polar,
title={Polar Collision Grids: Effective Interaction Modelling for Pedestrian Trajectory Prediction in Shared Space Using Collision Checks},
author={Golchoubian, Mahsa and Ghafurian, Moojan and Dautenhahn, Kerstin and Azad, Nasser Lashgarian},
booktitle={2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)},
pages={791--798},
year={2023},
organization={IEEE}
}This project is builds upon the codebase from social-lstm repsitory, developed by quancore as a pytorch implementation of the Social LSTM model proposed by Alahi et al. The Social LSTM model itself is also used as a baseline for comparison with our propsed CollisionGrid model.