[arXiv] What-If Motion Prediction for Autonomous Driving ❓🚗💨

Last update: Dec 29, 2022

Overview

WIMP - What If Motion Predictor

Reference PyTorch Implementation for What If Motion Prediction [PDF] [Dynamic Visualizations]

Setup

Requirements

The WIMP reference implementation and setup procedure has been tested to work with Ubuntu 16.04+ and has the following requirements:

python >= 3.7
pytorch >= 1.5.0

Installing Dependencies

Install remaining required Python dependencies using pip.
```
pip install -r requirements.txt
```
Install the Argoverse API module into the local Python environment by following steps 1, 2, and 4 in the README.

Argoverse Data

In order to set up the Argoverse dataset for training and evaluation, follow the steps below:

Download the the Argoverse Motion Forecasting v1.1 dataset and extract the compressed data subsets such that the raw CSV files are stored in the following directory structure:

├── WIMP
│   ├── src
│   ├── scripts
│   ├── data
│   │   ├── argoverse_raw
│   │   │   ├── train
│   │   │   │   ├── *.csv
│   │   │   ├── val
│   │   │   │   ├── *.csv
│   │   │   ├── test
│   │   │   │   ├── *.csv

Pre-process the raw Argoverse data into a WIMP-compatible format by running the following script. It should be noted that the Argoverse dataset is quite large and this script may take a few hours to run on a multi-threaded machine.

python scripts/run_preprocess.py --dataroot ./data/argoverse_raw/ \
--mode val --save-dir ./data/argoverse_processed --social-features \
--map-features --xy-features --normalize --extra-map-features \
--compute-all --generate-candidate-centerlines 6

Usage

For a detailed description of all possible configuration arguments, please run scripts with the -h flag.

Training

To train WIMP from scratch using a configuration similar to that reported in the paper, run a variant of the following command:

python src/main.py --mode train --dataroot ./data/argoverse_processed --IFC \
--lr 0.0001 --weight-decay 0.0 --non-linearity relu  --use-centerline-features \
--segment-CL-Encoder-Prob --num-mixtures 6 --output-conv --output-prediction \
--gradient-clipping --hidden-key-generator --k-value-threshold 10 \
--scheduler-step-size 60 90 120 150 180  --distributed-backend ddp \
--experiment-name example --gpus 4 --batch-size 25

Citing

If you've found this code to be useful, please consider citing our paper!

@article{khandelwal2020if,
  title={What-If Motion Prediction for Autonomous Driving},
  author={Khandelwal, Siddhesh and Qi, William and Singh, Jagjeet and Hartnett, Andrew and Ramanan, Deva},
  journal={arXiv preprint arXiv:2008.10587},
  year={2020}
}

Questions

This repo is maintained by William Qi and Siddhesh Khandelwal - please feel free to reach out or open an issue if you have additional questions/concerns.

We plan to clean up the codebase and add some additional utilities (possibly NuScenes data loaders and inference/visualization tools) in the near future, but don't expect to make significant breaking changes.

Comments

Pandas Error runpreprocess.py

Hello! First of all, thank you for making your code available for the readers of your great paper. I am having an issue while running run_preprocess.py. I think while reading the csv something goes wrong since my error is a pandas error. When I try to run the script, it gives me: KeyError: 'CITY_NAME' When I go to the script and give "MIA" as the CITY_NAME, just to see what happens, I receive a similar error: KeyError: 'OBJECT_TYPE' I checked the paths for the data. It seems fine. What could be the reason? Thank you!

opened by ahmetgurhan 0

Loss dimensions

Hi, thank you so much for your fantastic work.

Which is the order, and the dimensions, in this function?

def l1_ewta_loss(prediction, target, k=6, eps=1e-7, mr=2.0):
    num_mixtures = prediction.shape[1]

    target = target.unsqueeze(1).expand(-1, num_mixtures, -1, -1)
    l1_loss = nn.functional.l1_loss(prediction, target, reduction='none').sum(dim=[2, 3])

    # Get loss from top-k mixtures for each timestep
    mixture_loss_sorted, mixture_ranks = torch.sort(l1_loss, descending=False)
    mixture_loss_topk = mixture_loss_sorted.narrow(1, 0, k)

    # Aggregate loss across timesteps and batch
    loss = mixture_loss_topk.sum()
    loss = loss / target.size(0)
    loss = loss / target.size(2)
    loss = loss / k
    return loss

I am not able to obtain good results compared to NLL. I have as inputs:

predictions: batch_size x num_modes x pred_len x data_dim (e.g. 1024 x 6 x 30 x 2) gt: batch_size x pred_len x data_dim (e.g. 1024 x 30 x 2)

Is this correct?

opened by Cram3r95 0

Reproducing the Map-Free and only Social-Context Results form the Ablation Study

Hey there,

I want to reproduce the results of your ablation study, where you only used Social-Context with EWTA-Loss.

However, I habe problems training the model only with social context. What are the correct flags I need to set for preprocessing (run_preprocess.py) and for training (main.py)?

Looking forward hearing from you soon!

Best regards

SchDevel

opened by SchDevel 2
Can I get your inference/visualization code?

Hi, first of all, thanks for your awesome work and sharing that to us.

I tried to make inference/visualization code by myself, unfortunately, there were some problems.

Maybe library's mismatching, my insufficient coding skills, or something else.

So, can i get your inference/visualization code or even skeleton base code?

opened by raspbe34 3
What is the method for incomplete trajectories?
Hi, thanks for sharing your great work~ I am wondering how you deal with the incomplete trajectories problem (agents have less then 2 seconds of history).

I notice that for the neighboring agent wrt focal agent, you discard all the agents (code) if their trajectories are not complete

how would you deal with those incomplete trajectories for the focal agent? Did you use interpolation or some techniques?

Thanks!
opened by XHwind 0

Releases(1.0)

1.0(Oct 8, 2020)

Initial code release for WIMP.
Source code(tar.gz)
Source code(zip)

Owner

William Qi

Prediction @argoai

GitHub Repository

This is the official pytorch implementation of AutoDebias, an automatic debiasing method for recommendation.

AutoDebias This is the official pytorch implementation of AutoDebias, a debiasing method for recommendation system. AutoDebias is proposed in the pape

77 Nov 25, 2022

Adversarial Framework for (non-) Parametric Image Stylisation Mosaics

Fully Adversarial Mosaics (FAMOS) Pytorch implementation of the paper "Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Imag

120 Dec 24, 2022

Deep learning models for change detection of remote sensing images

Change Detection Models (Remote Sensing) Python library with Neural Networks for Change Detection based on PyTorch. ⚡ ⚡ ⚡ I am trying to build this pr

176 Dec 24, 2022

A unified 3D Transformer Pipeline for visual synthesis

Overview This is the official repo for the paper: "NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion". NÜWA is a unified multimodal

2.6k Jan 03, 2023

Easy and comprehensive assessment of predictive power, with support for neuroimaging features

Documentation: https://raamana.github.io/neuropredict/ News As of v0.6, neuropredict now supports regression applications i.e. predicting continuous t

93 Nov 29, 2022

AirCode: A Robust Object Encoding Method

AirCode This repo contains source codes for the arXiv preprint "AirCode: A Robust Object Encoding Method" Demo Object matching comparison when the obj

30 Dec 09, 2022

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

152 Jan 02, 2023

For medical image segmentation

LeViT_UNet For medical image segmentation Our model is based on LeViT (https://github.com/facebookresearch/LeViT). You'd better gitclone its codes. Th

13 Dec 24, 2022

A Tensorflow based library for Time Series Modelling with Gaussian Processes

Markovflow Documentation | Tutorials | API reference | Slack What does Markovflow do? Markovflow is a Python library for time-series analysis via prob

24 Dec 12, 2022

Soft actor-critic is a deep reinforcement learning framework for training maximum entropy policies in continuous domains.

This repository is no longer maintained. Please use our new Softlearning package instead. Soft Actor-Critic Soft actor-critic is a deep reinforcement

752 Jan 07, 2023

[arXiv] What-If Motion Prediction for Autonomous Driving ❓🚗💨

Related tags

Overview

WIMP - What If Motion Predictor

Setup

Requirements

Installing Dependencies

Argoverse Data

Usage

Training

Citing

Questions

Comments

Pandas Error runpreprocess.py

Loss dimensions

Reproducing the Map-Free and only Social-Context Results form the Ablation Study

Can I get your inference/visualization code?

What is the method for incomplete trajectories?

Releases(1.0)

1.0(Oct 8, 2020)

Owner

William Qi

This is the official pytorch implementation of AutoDebias, an automatic debiasing method for recommendation.

Adversarial Framework for (non-) Parametric Image Stylisation Mosaics

Deep learning models for change detection of remote sensing images

A unified 3D Transformer Pipeline for visual synthesis

Easy and comprehensive assessment of predictive power, with support for neuroimaging features

AirCode: A Robust Object Encoding Method

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

For medical image segmentation

A Tensorflow based library for Time Series Modelling with Gaussian Processes

Soft actor-critic is a deep reinforcement learning framework for training maximum entropy policies in continuous domains.

Code for the paper "PortraitNet: Real-time portrait segmentation network for mobile device" @ CAD&Graphics2019

Starter code for the ICCV 2021 paper, 'Detecting Invisible People'

Omnidirectional camera calibration in python

FaRL for Facial Representation Learning

A trusty face recognition research platform developed by Tencent Youtu Lab

Code release for The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification (TIP 2020)

Codebase for "ProtoAttend: Attention-Based Prototypical Learning."

code for Grapadora research paper experimentation

Contrastive Language-Image Pretraining

Implementation of Cross Transformer for spatially-aware few-shot transfer, in Pytorch