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

Sonia Baee, Erfan Pakdamanian, Inki Kim, Lu Feng, Vicente Ordonez, Laura Barnes.
International Conference on Computer Vision. ICCV 2021.
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abstract

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.

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Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021

citation

@inproceedings{baee2021medirl,
  title = {MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning.},
  author = {Baee, Sonia and Pakdamanian, Erfan and Kim, Inki and Feng, Lu and Ordonez, Vicente and Barnes, Laura},
  year = {2021},
  booktitle = {International Conference on Computer Vision. ICCV 2021},
  url = {https://arxiv.org/abs/1912.07773},
}