This repository is for generating datasets used in our project:

Ryo Yonetani*, Tatsunori Taniai*, Mohammadamin Barekatain, Mai Nishimura, Asako Kanezaki, "Path Planning using Neural A* Search", ICML, 2021 [paper] [project page]

• python3 (>=3.7.7)
• python3-venv

$ git submodule update --init --recursive    # if you forget --recursive option
$ python3 -m venv venv
$ source activate venv/bin/activate
$ pip install -e .

$ sh 0_MP.sh		# generate shortest path problem instances for MP dataset
$ sh 1_TiledMP.sh	# for Tiled MP dataset
$ sh 2_CSM.sh		# for CSM dataset
$ sh 3_SDD.sh		# generate image+pedestrian traj instances from Stanford Drone Dataset

If you want to fully reproduce our result for MP, TiledMP, and CSM datasets, please use the original data included in this repository.

• This repository includes some code from RLAgent/gated-path-planning-networks [1], with permission of the authors.
• MP and TiledMP datasets are created from mohakbhardwaj/motion_planning_datasets [2].
• CSM dataset is created using City/Street Maps in Pathfinding Benchmarks [3].
• SDD dataset is created using Stanford Drone Dataset [4] reorganized in crowdbotp/OpenTraj [5].

• [1] Lisa Lee*, Emilio Parisotto*, Devendra Singh Chaplot, Eric Xing, Ruslan Salakhutdinov, "Gated Path Planning Networks", ICML, 2018.
• [2] Mohak Bhardwaj, Sanjiban Choudhury, Sebastian Scherer, "Learning Heuristic Search via Imitation", CoRL, 2017.
• [3] Nathan Sturtevant, "Benchmarks for Grid-Based Pathfinding", Transactions on Computational Intelligence and AI in Games, 2012.
• [4] Alexandre Robicquet, Amir Sadeghian, Alexandre Alahi, Silvio Savarese, "Learning social etiquette: Human trajectory understanding in crowded scenes", ECCV, 2016.
• [5] Javad Amirian, Bingqing Zhang, Francisco Valente Castro, Juan Jose Baldelomar, Jean-Bernard Hayet, Julien Pettre, "OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets", ACCV, 2020.