[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

High level network definitions with pre-trained weights in TensorFlow

TensorNets High level network definitions with pre-trained weights in TensorFlow (tested with 2.1.0 = TF = 1.4.0). Guiding principles Applicability.

1k Dec 13, 2022

RIFE - Real-Time Intermediate Flow Estimation for Video Frame Interpolation

RIFE - Real-Time Intermediate Flow Estimation for Video Frame Interpolation YouTube | BiliBili 16X interpolation results from two input images: Introd

28 Dec 09, 2022

Source code for our paper "Empathetic Response Generation with State Management"

Source code for our paper "Empathetic Response Generation with State Management" this repository is maintained by both Jun Gao and Yuhan Liu Model Ove

3 Oct 08, 2022

Supplementary materials for ISMIR 2021 LBD paper "Evaluation of Latent Space Disentanglement in the Presence of Interdependent Attributes"

Evaluation of Latent Space Disentanglement in the Presence of Interdependent Attributes Supplementary materials for ISMIR 2021 LBD submission: K. N. W

2 Oct 25, 2021

Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation

SUO-SLAM This repository hosts the code for our CVPR 2022 paper "Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation". ArXiv li

97 Jan 03, 2023

covid question answering datasets and fine tuned models

Covid-QA Fine tuned models for question answering on Covid-19 data. Hosted Inference This model has been contributed to huggingface.Click here to see

19 Sep 09, 2021

Notepy is a full-featured Notepad Python app

Notepy A full featured python text-editor Notable features Autocompletion for parenthesis and quote Auto identation Syntax highlighting Compile and ru

11 Sep 28, 2022

An Object Oriented Programming (OOP) interface for Ontology Web language (OWL) ontologies.

Enabling a developer to use Ontology Web Language (OWL) along with its reasoning capabilities in an Object Oriented Programming (OOP) paradigm, by pro

7 Sep 23, 2022

An official PyTorch implementation of the TKDE paper "Self-Supervised Graph Representation Learning via Topology Transformations".

Self-Supervised Graph Representation Learning via Topology Transformations This repository is the official PyTorch implementation of the following pap

2 Oct 31, 2022

Employee-Managment - Company employee registration software in the face recognition system

Employee-Managment Company employee registration software in the face recognitio

7 Jul 10, 2022

Semi-Supervised Learning for Fine-Grained Classification

Semi-Supervised Learning for Fine-Grained Classification This repo contains the code of: A Realistic Evaluation of Semi-Supervised Learning for Fine-G

25 Nov 08, 2022

Implementation of Bagging and AdaBoost Algorithm

Bagging-and-AdaBoost Implementation of Bagging and AdaBoost Algorithm Dataset Red Wine Quality Data Sets For simplicity, we will have 2 classes of win

1 Nov 01, 2021

DAFNe: A One-Stage Anchor-Free Deep Model for Oriented Object Detection

DAFNe: A One-Stage Anchor-Free Deep Model for Oriented Object Detection Code for our Paper DAFNe: A One-Stage Anchor-Free Deep Model for Oriented Obje

58 Dec 19, 2022

Learning Spatio-Temporal Transformer for Visual Tracking

STARK The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking Hiring research interns for visual transformer

484 Dec 29, 2022

[WACV21] Code for our paper: Samuel, Atzmon and Chechik, "From Generalized zero-shot learning to long-tail with class descriptors"

DRAGON: From Generalized zero-shot learning to long-tail with class descriptors Paper Project Website Video Overview DRAGON learns to correct the bias

25 Dec 06, 2022

Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties

Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties 8.11.2021 Andrij Vasylenko I

4 Dec 20, 2022