MEDIRL: Predicting the Visual Attention of Drivers via Deep Inverse Reinforcement Learning

Sonia Baee, Erfan Pakdamanian, Inki Kim, Lu Feng, Vicente Ordonez, Laura Barnes

Published at ICCV-2021!

This repo is under construction and we will add the clean and most up to dated code shortly. Stay tuned!

To - Do

• [ ] Revise the code and add the missed functions
• [ ] Add the final dataset
• [ ] Change the format of the data to the .json file (EyeCar)
• [ ] Add the trained models

Overview

Inspired by human visual attention, we propose a novel inverse reinforcement learning formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for predicting the visual attention of drivers in accident-prone situations. MEDIRL predicts fixation locations that lead to maximal rewards by learning a task-sensitive reward function from eye fixation patterns recorded from attentive drivers. Additionally, we introduce EyeCar, a new driver attention dataset in accident-prone situations. We conduct comprehensive experiments to evaluate our proposed model on three common benchmarks: (DR(eye)VE, BDD-A, DADA-2000), and our EyeCar dataset. Results indicate that MEDIRL outperforms existing models for predicting attention and achieves state-of-the-art performance. We present extensive ablation studies to provide more insights into different features of our proposed model. The code and dataset are provided for reproducibility.

Dataset - EyeCar

We select 21 front-view videos that were captured in various traffic, weather, and day light conditions. Each video is 30sec in length and contains typical driving tasks (e.g., lane-keeping, merging-in, and braking) ending to rear-end collisions. Note that all the conditions were counterbalanced among all the participants. Moreover, EyeCar provides information about the speed and GPS of the ego-vehicle. In addition, each video frame comprises 4.6 vehicles on average, making EyeCar driving scenes more complex than other visual attention datasets. The EyeCar dataset contains 3.5h of gaze behavior (aggregated and raw) from the 20 participants, as well as more than 315,000 rear-end collisions video frames. In EyeCar dataset, we account for the sequence of eye fixations, and thus we emphasize on attention shift to the salient regions in a complex driving scene. EyeCar also provides a rich set of annotations (e.g., scene tagging, object bounding, lane marking, etc.). You can accesss to the data in this EyeCar. Compared to prior datasets, EyeCar is the only dataset captured from a point-of-view (POV) perspective, involving collisions, and including metadata for both speedand GPS. EyeCar also has the largest average number of vehicles per scene, and gaze data for 20 participants.

Requirements

The simplest way to clone the development environment is with the anaconda-project and conda-env systems; these can be installed with Anaconda with the command

conda install anaconda anaconda-project

All necessary packages are found in anaconda-project.yml. To create an Anaconda virtual environment, run

rm environment.yml #if `environment.yml` already exists
anaconda-project export-env-spec -n medirl ./environment.yml
conda-env create -n medirl -f environment.yml
conda activate medirl

To deactivate the virtual environment, run

conda deactivate

To run a jupyter notebook inside this virtual environment, activate the virtual env medirl and run

(medirl)$ python -m ipykernel install --user --name=medirl

where the (medirl) prefix indicates that you are currently in the medirl virtual environment. Then, select the "medirl" kernel after creating or opening a jupyter notebook.

Model

You can run the main script to get the result of each component:

python main.py

We saved the best/last of models of each module in Models folder.

Data

You can accesss to the data in this EyeCar

Citing

If you find this paper/code useful, please consider citing our paper:

  @InProceedings{Baee_2021_ICCV,
    author    = {Baee, Sonia and Pakdamanian, Erfan and Kim, Inki and Feng, Lu and Ordonez, Vicente and Barnes, Laura},
    title     = {MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {13178-13188}
}