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.

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"
}