self-driving.md

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Home  | Behavioral  | Applications  | Datasets  

Scene gaze  | In-vehicle gaze  | Distraction detection  | Drowsiness detection  | Action anticipation  | Driver awareness  | Self-driving  | Papers with code  

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Click on each entry below to see additional information.

Araluce et al., Leveraging Driver Attention for an End-to-End Explainable Decision-Making FromFrontal Images, Trans. ITS, 2023 | paper

Dataset(s): BDD-OIA

@article{2023_T-ITS_Araluce,
    author = "Araluce, Javier and Bergasa, Luis M and Oca{\\textasciitilde n}a, Manuel and Llamazares, {\'A}ngel and L{\'o}pez-Guill{\'e}n, Elena",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Leveraging Driver Attention for an End-to-End Explainable Decision-Making From Frontal Images",
    year = "2024"
}

Zhao et al., Improving Autonomous Vehicle Visual Perception by Fusing Human Gaze and Machine Vision, Trans. ITS, 2023 | paper

Dataset(s): private

@article{2023_T-ITS_Zhao,
    author = "Zhao, Yiyue and Lei, Cailin and Shen, Yu and Du, Yuchuan and Chen, Qijun",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Improving Autonomous Vehicle Visual Perception by Fusing Human Gaze and Machine Vision",
    year = "2023"
}

Niu et al., Auditory and visual warning information generation of the risk object in driving scenes based on weakly supervised learning, IV, 2022 | paper

Dataset(s): private

@inproceedings{2022_IV_Niu,
    author = "Niu, Yinjie and Ding, Ming and Zhang, Yuxiao and Ohtani, Kento and Takeda, Kazuya",
    booktitle = "2022 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1572--1577",
    title = "Auditory and visual warning information generation of the risk object in driving scenes based on weakly supervised learning",
    year = "2022"
}

Li et al., Enhancement of Target Feature Regions and Intention-Driven Visual Attention Selection in Traffic Scenes, IV, 2022 | paper

Dataset(s): KITTI

@inproceedings{2022_IV_Li,
    author = "Li, Jing and Zhang, Dongbo and Meng, Bumin and Chen, Renjie and Tang, Jiajun and Wang, Yaonan",
    booktitle = "2022 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "404--410",
    title = "Enhancement of Target Feature Regions and Intention-Driven Visual Attention Selection in Traffic Scenes",
    year = "2022"
}

Li et al., Important Object Identification with Semi-Supervised Learning for Autonomous Driving, ICRA, 2022 | paper

Dataset(s): H3D

@inproceedings{2022_ICRA_Li,
    author = "Li, Jiachen and Gang, Haiming and Ma, Hengbo and Tomizuka, Masayoshi and Choi, Chiho",
    booktitle = "2022 International Conference on Robotics and Automation (ICRA)",
    organization = "IEEE",
    pages = "2913--2919",
    title = "Important object identification with semi-supervised learning for autonomous driving",
    year = "2022"
}

Wei et al., Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving, ICRA, 2021 | paper

Dataset(s): Drive4D, nuScenes

@inproceedings{2021_ICRA_Wei,
    author = "Wei, Bob and Ren, Mengye and Zeng, Wenyuan and Liang, Ming and Yang, Bin and Urtasun, Raquel",
    booktitle = "ICRA",
    title = "Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving",
    year = "2021"
}

Chitta et al., NEAT: Neural Attention Fields for End-to-End Autonomous Driving, ICCV, 2021 | paper | code

Dataset(s): CARLA

@inproceedings{2021_ICCV_Chitta,
    author = "Chitta, Kashyap and Prakash, Aditya and Geiger, Andreas",
    booktitle = "ICCV",
    title = "NEAT: Neural Attention Fields for End-to-End Autonomous Driving",
    year = "2021"
}

Ishihara et al., Multi-task Learning with Attention for End-to-end Autonomous Driving, CVPRW, 2021 | paper

Dataset(s): CARLA

@inproceedings{2021_CVPRW_Ishihara,
    author = "Ishihara, Keishi and Kanervisto, Anssi and Miura, Jun and Hautamaki, Ville",
    booktitle = "CVPRW",
    title = "Multi-task Learning with Attention for End-to-end Autonomous Driving",
    year = "2021"
}

Prakash et al., Multi-Modal Fusion Transformer for End-to-End Autonomous Driving, CVPR, 2021 | paper | code

Dataset(s): CARLA

@inproceedings{2021_CVPR_Prakash,
    author = "Prakash, Aditya and Chitta, Kashyap and Geiger, Andreas",
    booktitle = "CVPR",
    title = "Multi-Modal Fusion Transformer for End-to-End Autonomous Driving",
    year = "2021"
}

Xia et al., Periphery-Fovea Multi-Resolution Driving Model Guided by Human Attention, WACV, 2020 | paper | code

Dataset(s): BDD-X, BDD-A, DR(eye)VE

@inproceedings{2020_WACV_Xia,
    author = "Xia, Ye and Kim, Jinkyu and Canny, John and Zipser, Karl and Canas-Bajo, Teresa and Whitney, David",
    booktitle = "WACV",
    title = "Periphery-fovea multi-resolution driving model guided by human attention",
    year = "2020"
}

Li et al., End-to-end Contextual Perception and Prediction with Interaction Transformer, IROS, 2020 | paper

Dataset(s): ATG4D, nuScenes

@inproceedings{2020_IROS_Li_1,
    author = "Li, Lingyun Luke and Yang, Bin and Liang, Ming and Zeng, Wenyuan and Ren, Mengye and Segal, Sean and Urtasun, Raquel",
    booktitle = "IROS",
    title = "End-to-end contextual perception and prediction with interaction transformer",
    year = "2020"
}

Mittal et al., AttnGrounder: Talking to Cars with Attention, ECCVW, 2020 | paper | code

Dataset(s): Talk2Car

@inproceedings{2020_ECCVW_Mittal,
    author = "Mittal, Vivek",
    booktitle = "ECCV",
    title = "Attngrounder: Talking to cars with attention",
    year = "2020"
}

Zhou et al., DA4AD: End-to-end deep attention-based visual localization for autonomous driving, ECCV, 2020 | paper

Dataset(s): Apollo-DaoxiangLake

@inproceedings{2020_ECCV_Zhou,
    author = "Zhou, Yao and Wan, Guowei and Hou, Shenhua and Yu, Li and Wang, Gang and Rui, Xiaofei and Song, Shiyu",
    booktitle = "ECCV",
    title = "DA4AD: End-to-end deep attention-based visual localization for autonomous driving",
    year = "2020"
}

Kim et al., Attentional Bottleneck: Towards an Interpretable Deep Driving Network, CVPRW, 2020 | paper

Dataset(s): private

@inproceedings{2020_CVPRW_Kim,
    author = "Kim, Jinkyu and Bansal, Mayank",
    booktitle = "CVPR",
    title = "Attentional bottleneck: Towards an interpretable deep driving network",
    year = "2020"
}

Cultrera et al., Explaining Autonomous Driving by Learning End-to-End Visual Attention, CVPRW, 2020 | paper

Dataset(s): CARLA

@inproceedings{2020_CVPRW_Cultrera,
    author = "Cultrera, Luca and Seidenari, Lorenzo and Becattini, Federico and Pala, Pietro and Del Bimbo, Alberto",
    booktitle = "CVPRW",
    title = "{Explaining Autonomous Driving by Learning End-to-End Visual Attention}",
    year = "2020"
}

Kim et al., Advisable Learning for Self-driving Vehicles by Internalizing Observation-to-Action Rules, CVPR, 2020 | paper | code

Dataset(s): BDD-X, CARLA

@inproceedings{2020_CVPR_Kim,
    author = "Kim, Jinkyu and Moon, Suhong and Rohrbach, Anna and Darrell, Trevor and Canny, John",
    booktitle = "CVPR",
    title = "Advisable learning for self-driving vehicles by internalizing observation-to-action rules",
    year = "2020"
}

Mori et al., Visual Explanation by Attention Branch Network for End-to-end Learning-based Self-driving, IV, 2019 | paper

Dataset(s): private

@inproceedings{2019_IV_Mori,
    author = "Mori, Keisuke and Fukui, Hiroshi and Murase, Takuya and Hirakawa, Tsubasa and Yamashita, Takayoshi and Fujiyoshi, Hironobu",
    booktitle = "IV",
    title = "Visual explanation by attention branch network for end-to-end learning-based self-driving",
    year = "2019"
}

Chen et al., Gaze Training by Modulated Dropout Improves Imitation Learning, IROSW, 2019 | paper

Dataset(s): TORCS

@inproceedings{2019_IROSW_Chen,
    author = "Chen, Yuying and Liu, Congcong and Tai, Lei and Liu, Ming and Shi, Bertram E",
    booktitle = "IROS",
    title = "Gaze training by modulated dropout improves imitation learning",
    year = "2019"
}

Wang et al., Deep Object-Centric Policies for Autonomous Driving, ICRA, 2019 | paper

Dataset(s): BDD

@inproceedings{2019_ICRA_Wang,
    author = "Wang, Dequan and Devin, Coline and Cai, Qi-Zhi and Yu, Fisher and Darrell, Trevor",
    booktitle = "ICRA",
    title = "Deep object-centric policies for autonomous driving",
    year = "2019"
}

Li et al., DBUS: Human Driving Behavior Understanding System, ICCVW, 2019 | paper

Dataset(s): private

@inproceedings{2019_ICCVW_Li,
    author = "Li, Max Guangyu and Jiang, Bo and Che, Zhengping and Shi, Xuefeng and Liu, Mengyao and Meng, Yiping and Ye, Jieping and Liu, Yan",
    booktitle = "ICCVW",
    title = "DBUS: Human Driving Behavior Understanding System.",
    year = "2019"
}

Kim et al., Grounding Human-to-Vehicle Advice for Self-driving Vehicles, CVPR, 2019 | paper

Dataset(s): HAD

@inproceedings{2019_CVPR_Kim,
    author = "Kim, Jinkyu and Misu, Teruhisa and Chen, Yi-Ting and Tawari, Ashish and Canny, John",
    booktitle = "CVPR",
    title = "Grounding human-to-vehicle advice for self-driving vehicles",
    year = "2019"
}

Liu et al., A Gaze Model Improves Autonomous Driving, ACM Symposium on Eye Tracking Research and Applications, 2019 | paper

Dataset(s): TORCS

@inproceedings{2019_ACM_Liu,
    author = "Liu, Congcong and Chen, Yuying and Tai, Lei and Ye, Haoyang and Liu, Ming and Shi, Bertram E",
    booktitle = "ETRA",
    title = "A gaze model improves autonomous driving",
    year = "2019"
}

Mund et al., Visualizing the Learning Progress of Self-Driving Cars, ITSC, 2018 | paper

Dataset(s): Dashcam dataset

@inproceedings{2018_ITSC_Mund,
    author = {Mund, Sandro and Frank, Rapha{\"e}l and Varisteas, Georgios and State, Radu},
    booktitle = "ITSC",
    title = "{Visualizing the Learning Progress of Self-Driving Cars}",
    year = "2018"
}

He et al., Aggregated Sparse Attention for Steering Angle Prediction, ICPR, 2018 | paper

Dataset(s): DIPLECS, Comma.ai

@inproceedings{2018_ICPR_He,
    author = "He, Sen and Kangin, Dmitry and Mi, Yang and Pugeault, Nicolas",
    booktitle = "ICPR",
    title = "{Aggregated Sparse Attention for Steering Angle Prediction}",
    year = "2018"
}

Kim et al., Textual Explanations for Self-Driving Vehicles, ECCV, 2018 | paper | code

Dataset(s): BDD-X

@inproceedings{2018_ECCV_Kim,
    author = "Kim, Jinkyu and Rohrbach, Anna and Darrell, Trevor and Canny, John and Akata, Zeynep",
    booktitle = "ECCV",
    title = "Textual explanations for self-driving vehicles",
    year = "2018"
}

Kim et al., Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention, ICCV, 2017 | paper

Dataset(s): Comma.ai, Udacity, private

@inproceedings{2017_ICCV_Kim,
    author = "Kim, Jinkyu and Canny, John",
    booktitle = "ICCV",
    title = "Interpretable learning for self-driving cars by visualizing causal attention",
    year = "2017"
}

Bojarski et al., Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car, arXiv, 2017 | paper | code

Dataset(s): private

@article{2017_arXiv_Bojarski,
    author = "Bojarski, Mariusz and Yeres, Philip and Choromanska, Anna and Choromanski, Krzysztof and Firner, Bernhard and Jackel, Lawrence and Muller, Urs",
    journal = "arXiv:1704.07911",
    title = "Explaining how a deep neural network trained with end-to-end learning steers a car",
    year = "2017"
}