MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning
This work addresses improving safety in autonomous driving by predicting driver attention, but it is incremental as it builds on existing inverse reinforcement learning methods with a new dataset.
The paper tackles predicting driver visual attention in accident-prone situations by proposing MEDIRL, a maximum entropy deep inverse reinforcement learning model, and introduces a new dataset called EyeCar; results show it outperforms existing models and achieves state-of-the-art performance on multiple benchmarks.
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.