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

Related tags

Deep Learningpredictionself-driving-carautonomous-drivingautonomous-vehiclestrajectory-prediction
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:

  1. python >= 3.7
  2. pytorch >= 1.5.0

Installing Dependencies

  1. Install remaining required Python dependencies using pip.

    pip install -r requirements.txt

  2. 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:

  1. 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

  2. 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

    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

    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

    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.

    image

    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?

    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?

    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).

    1. I notice that for the neighboring agent wrt focal agent, you discard all the agents (code) if their trajectories are not complete
    2. 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)
Owner
William Qi
Prediction @argoai
William Qi
GitHub Repository
TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision

TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision

52 Dec 23, 2022
Equipped customers with insights about their EVs Hourly energy consumption and helped predict future charging behavior using LSTM model

Equipped customers with insights about their EVs Hourly energy consumption and helped predict future charging behavior using LSTM model. Designed sample dashboard with insights and recommendation for

Yash 2 Apr 07, 2022
Implementation of TimeSformer, a pure attention-based solution for video classification

TimeSformer - Pytorch Implementation of TimeSformer, a pure and simple attention-based solution for reaching SOTA on video classification.

Phil Wang 602 Jan 03, 2023
PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English

PASTRIE Official release of the corpus described in the paper: Michael Kranzlein, Emma Manning, Siyao Peng, Shira Wein, Aryaman Arora, and Nathan Schn

NERT @ Georgetown 4 Dec 02, 2021
A stable algorithm for GAN training

DRAGAN (Deep Regret Analytic Generative Adversarial Networks) Link to our paper - https://arxiv.org/abs/1705.07215 Pytorch implementation (thanks!) -

195 Oct 10, 2022
The aim of the game, as in the original one, is to find a specific image from a group of different images of a person's face

GUESS WHO Main Links: [Github] [App] Related Links: [CLIP] [Celeba] The aim of the game, as in the original one, is to find a specific image from a gr

Arnau - DIMAI 3 Jan 04, 2022
Code for the paper "Implicit Representations of Meaning in Neural Language Models"

Implicit Representations of Meaning in Neural Language Models Preliminaries Create and set up a conda environment as follows: conda create -n state-pr

Belinda Li 39 Nov 03, 2022
AlgoVision - A Framework for Differentiable Algorithms and Algorithmic Supervision

NeurIPS 2021 Paper "Learning with Algorithmic Supervision via Continuous Relaxations"

Felix Petersen 76 Jan 01, 2023
Code for paper [ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot] (ICCV 2021, oral))

ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot This repository is the official PyTorch implementation of ICCV-21 pape

Jiarui 21 May 09, 2022
A Deep Learning Framework for Neural Derivative Hedging

NNHedge NNHedge is a PyTorch based framework for Neural Derivative Hedging. The following repository was implemented to ease the experiments of our pa

GUIJIN SON 17 Nov 14, 2022
Neon: an add-on for Lightbulb making it easier to handle component interactions

Neon Neon is an add-on for Lightbulb making it easier to handle component interactions. Installation pip install git+https://github.com/neonjonn/light

Neon Jonn 9 Apr 29, 2022
This is a student data management application developed in Python and TKinter. It utilizes the TKinter pillow library to include images to buttons. I've separated TKinter elements into their own individual classes. The user can change the smilely face color for each button individually or by entire row.

Smiley Face Cube Display Table of Contents Project Description Getting Started Prerequisites Installation & Deployment Additional Documentation Projec

Yohan (John) Cho 0 Aug 04, 2021
Isaac Gym Reinforcement Learning Environments

Isaac Gym Reinforcement Learning Environments

NVIDIA Omniverse 714 Jan 08, 2023
Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience This repository is the official implementation of [https://www.bi

Eulerlab 6 Oct 09, 2022
Official implementation of "Articulation Aware Canonical Surface Mapping"

Articulation-Aware Canonical Surface Mapping Nilesh Kulkarni, Abhinav Gupta, David F. Fouhey, Shubham Tulsiani Paper Project Page Requirements Python

Nilesh Kulkarni 56 Dec 16, 2022
[ICCV '21] In this repository you find the code to our paper Keypoint Communities

Keypoint Communities In this repository you will find the code to our ICCV '21 paper: Keypoint Communities Duncan Zauss, Sven Kreiss, Alexandre Alahi,

Duncan Zauss 262 Dec 13, 2022
The self-supervised goal reaching benchmark introduced in Discovering and Achieving Goals via World Models

Lexa-Benchmark Codebase for the self-supervised goal reaching benchmark introduced in 'Discovering and Achieving Goals via World Models'. Setup Create

1 Oct 14, 2021
A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21

ANEMONE A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21 Dependencies python==3.6.1 dgl==

Graph Analysis & Deep Learning Laboratory, GRAND 30 Dec 14, 2022
Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations)

Graph Neural Networks with Learnable Structural and Positional Representations Source code for the paper "Graph Neural Networks with Learnable Structu

Vijay Prakash Dwivedi 180 Dec 22, 2022
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Little Ball of Fur is a graph sampling extension library for Python. Please look at the Documentation, relevant Paper, Promo video and External Resour

Benedek Rozemberczki 619 Dec 14, 2022