/fvl-ICRA2019

Official code for future vehicle localization paper implemented in Keras

Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems

Yu Yao, Mingze Xu, Chiho Choi, David J. Crandall, Ella M. Atkins, and Behzad Dariush

Introduction

This repo contains the source code of future vehicle localization (FVL)[1], implemented in Keras with tensorflow backend.

💥 ATTENTION (05/20/2019): The HEV-I dataset is now available. Please follow the instructions on the website to get access to it.

💥 ATTENTION (03/2019): The current repo is a placeholder. The original Keras code is not released yet due to the HRI authority issue. The readers are redirected to a pytorch implementation of the paper.

Following dependencies (or newer version):

python3.5 or python3.6
tensorflow-gpu=1.1.0
keras=1.1.0.

The RNN encoder-decoder model:

To train the model, run

cd train
sh run_train.sh

Check the command line arguments by python train.py --help

To test the trained model, run

cd test
sh run_test.sh

We tested our model

Test results:

Test results on HEV-I dataset:

Models Easy Cases Challenging Cases All Cases
Linear 31.49 / 17.04 / 0.68 107.93 / 56.29 / 0.33 72.37 / 38.04 / 0.50
ConstAccel 20.82 / 13.86 / 0.74 90.33 / 49.06 / 0.35 58.00 / 28.05 / 0.53
Conv1D [2] 18.84 / 12.09 / 0.75 37.95 / 20.97 / 0.64 29.06 / 16.84 / 0.69
RNN-ED-X 23.57 / 11.96 / 0.74 43.15 / 22.24 / 0.60 34.04 / 17.46 / 0.67
RNN-ED-XE 22.28 / 11.60 / 0.74 42.27 / 22.39 / 0.61 32.97 / 17.37 / 0.67
RNN-ED-XO 17.45 / 8.68 / 0.78 32.61 / 16.72 / 0.66 25.56 / 12.98 / 0.72
RNN-ED-XOE 16.72 / 8.52 / 0.80 32.05 / 16.63 / 0.66 24.92 / 12.86 / 0.73

Test results on KITTI dataset:

Models FDE ADE FIOU
Linear 78.19 38.21 0.33
ConstAccel 55.66 25.78 0.39
Conv1D [2] 44.13 24.38 0.49
Ours 37.11 17.88 0.53

Dataset Demo

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Prediction Demo

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Citation

If you found the repo is useful, please feel free to cite our paper:

@article{yao2018egocentric,
title={Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems},
author={Yao, Yu and Xu, Mingze and Choi, Chiho and Crandall, David J and Atkins, Ella M and Dariush, Behzad},
journal={arXiv preprint arXiv:1809.07408},
year={2018}
}

Reference

[1] Yao Y, Xu M, Choi C, Crandall DJ, Atkins EM, Dariush B. Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems. arXiv preprint arXiv:1809.07408. 2018 Sep 19.

[2] Yagi T, Mangalam K, Yonetani R, Sato Y. Future person localization in first-person videos. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018 (pp. 7593-7602).