Unified Pedestrian Path Prediction Framework

This is the code for the paper Unified Pedestrian Path Prediction Framework: A Comparison Study

Setup

All code was developed and tested on Ubuntu 20.04 with Python 3.7 and PyTorch 1.10.

You can setup a virtual environment to run the code like this:

python3.7 -m venv env               # Create a virtual environment
source env/bin/activate           # Activate virtual environment
pip install -r requirements.txt   # Install dependencies

Training new models

You can train your own model by following these instructions:

Step 1: Data

The directory datasets/<dataset_name> contains train/ val/ and test/ splits. All the datasets are pre-processed to be in world coordinates i.e. in meters. We support five datasets ETH, ZARA1, ZARA2, HOTEL and UNIV. We use leave-one-out approach, train on 4 sets and test on the remaining set. We observe the trajectory for 8 times steps (3.2 seconds) and show prediction results for 8 (3.2 seconds) and 12 (4.8 seconds) time steps.

Step 2: Train a model

Now you can train a new model by running the script:

python scripts/train_gail.py

By default this will train a model on ETH, periodically saving checkpoint files saved_model_ADE.pt and saved_model_FDE.pt for the lowest validation ADE/FDE separately, to the current working directory. The training script has a number of command-line flags that you can use to configure the model architecture, hyperparameters, and input / output settings:

Framework settings

• --randomness_definition: Policy stochasticity: Stochastic or Deterministic
• --step_definition: Decision-making process: One-time or Sequential
• --loss_definition: Distance function: Squared L2-norm or Discriminator
• --discount_factor: Future awareness: γ = 0 or γ /= 0

Hyper-parameters and algorithm settings

• --training_algorithm: Choose which RL updating algorithm, either "reinforce", "baseline" or "ppo" or "ppo_only". Default is 'reinforce'.
• --trainable_noise: Add a noise to the input during training. Default is False.
• --ppo-iterations: Number of PPO iterations. Default is 1.
• --ppo-clip: Amount of PPO clipping. Default is 0.2.
• --learning-rate: Learning rate. Default is 1e-5.
• --batch_size: Number of sequences in a batch (can be multiple paths). Default is 8.
• --log-std: Log std for the policy. Default is -2.99 (std = 0.05).
• --num_epochs: Number of times the model sees all data. Default is 200.

Dataset runs and model saving

• --seeding: Turn seeding on or off. Default is True.
• --seed: Random seed. Default is 0.
• --multiple_executions: Turn multiple runs on or off. Default is True.
• --runs: Number of times the script runs. Default is 5.
• --all_datasets: Run the script for all 5 datasets at once or not. Default is True.
• --dataset_name: Choose which dataset to train for. Default is eth.
• --check_testset: Also evaluate on the testset, next to validation set. Default is True.
• --output_dir: Path where models are saved. Default is current directory.
• --save_model_name_ADE: Name of the saved model with best ADE. Default is saved_model_ADE.
• --save_model_name_FDE: Name of the saved model with best FDE. Default is saved_model_FDE.
• --num_samples_check: Limit the number of samples during metric calculation. Default is 5000.
• --check_validation_every: Check the metrics on the validation dataset every X epochs. Default is 1.

Additional settings

• --obs_len: How many timesteps used for observation. Default is 8.
• --pred_len: How many timesteps used for prediction. Default is 12.
• --discriminator_steps: How many discriminator updates per iteration. Default is 1.
• --policy_steps: How many policy updates per iteration. Default is 1.
• --loader_num_workers: Number cpu/gpu processes. Default is 0.
• --skip: Number of frames to skip while making the dataset. Default is 1.
• --delim: How to read the data text file spacing. Default is \t.
• --l2_loss_weight: L2 loss multiplier. Default is 0.
• --use_gpu: How many GPUs to use (0 is cpu only). Default is 1.
• --load_saved_model: Path of pre-trained model. Default is None (don't use pretrained model).

Running Models

You can use the script scripts/evaluate_model.py to easily run any of the pretrained models on any of the datsets. Copying your models to /models/irl-models, in combination with the correct settings will compute the metrics for each model individually. Copying your models to /models/model_average will give you the averaged results. The evaluation script can be configured with the following settings:

• --dataset_name: Choose which dataset to evaluate for. Default is eth.
• --model_path: Directory of the models. Default is "../models/irl-models".
• --num_samples: Best-of-k for mADE/mFDE metrics. Default is 20.
• --dset_type: Which dataset split to evaluate on. Default is test.
• --model_average: Compute the average of the models or not. Default is True.
• --runs: Number of models to take the average of. Default is 5.
• --prediction_steps: Number of future trajectory points to predict. Default is 12.
• --noise: Add noise to the input or not. Default is False.