papers_with_code.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.

Action anticipation

Kung et al., Looking Inside Out: Anticipating Driver Intent From Videos, ICRA, 2024 | paper | code

Dataset(s): Brains4Cars

@inproceedings{2024_ICRA_Kung,
    author = "Kung, Yung-Chi and Zhang, Arthur and Wang, Junmin and Biswas, Joydeep",
    booktitle = "2024 IEEE International Conference on Robotics and Automation (ICRA)",
    organization = "IEEE",
    pages = "5608--5614",
    title = "Looking Inside Out: Anticipating Driver Intent From Videos",
    year = "2024"
}

Pardo-Decimavilla et al., Do You Act Like You Talk? Exploring Pose-based Driver Action Classification with Speech Recognition Networks, IV, 2024 | paper | code

Dataset(s): Drive&Act

@inproceedings{2024_IV_Pardo-Decimavilla,
    author = "Pardo-Decimavilla, Pablo and Bergasa, Luis M and Montiel-Mar{\'\i}n, Santiago and Antunes, Miguel and Llamazares, {\'A}ngel",
    booktitle = "2024 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1395--1400",
    title = "Do You Act Like You Talk? Exploring Pose-based Driver Action Classification with Speech Recognition Networks",
    year = "2024"
}

Tanama et al., Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained Environments, IROS, 2023 | paper | code

Dataset(s): Drive&Act

@inproceedings{2023_IROS_Tanama,
    author = "Tanama, Calvin and Peng, Kunyu and Marinov, Zdravko and Stiefelhagen, Rainer and Roitberg, Alina",
    booktitle = "2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
    organization = "IEEE",
    pages = "5479--5486",
    title = "Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained Environments",
    year = "2023"
}

Peng et al., TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration, IROS, 2022 | paper | code

Dataset(s): Drive&Act

@inproceedings{2022_IROS_Peng,
    author = "Peng, Kunyu and Roitberg, Alina and Yang, Kailun and Zhang, Jiaming and Stiefelhagen, Rainer",
    booktitle = "2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
    organization = "IEEE",
    pages = "278--285",
    title = "TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration",
    year = "2022"
}

Jain et al., Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture, ICRA, 2016 | paper | code

Dataset(s): Brain4Cars

@inproceedings{2016_ICRA_Jain,
    author = "Jain, Ashesh and Singh, Avi and Koppula, Hema S and Soh, Shane and Saxena, Ashutosh",
    booktitle = "ICRA",
    title = "Recurrent neural networks for driver activity anticipation via sensory-fusion architecture",
    year = "2016"
}

Scene gaze

Jin et al., MTSF: Multi-Scale Temporal–Spatial Fusion Network for Driver Attention Prediction, Trans. ITS, 2025 | paper | code

Dataset(s): DADA-2000, TDV, DR(eye)VE

@article{2025_T-ITS_Jin,
    author = "Jin, Lisheng and Ji, Bingdong and Guo, Baicang and Wang, Huanhuan and Han, Zhuotong and Liu, Xingchen",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "MTSF: Multi-Scale Temporal--Spatial Fusion Network for Driver Attention Prediction",
    year = "2024"
}

Sun et al., Towards Robust Autonomous Driving: Conditional Multimodal Large Language Models for Fine-Grained Perception, ICRA, 2025 | paper | code

Dataset(s): DriveLM, CODA-LM

@inproceedings{2025_ICRA_Sun,
    author = "Sun, Fengzhao and Yu, Jun and Zhang, Yunxiang and Hou, Jiaming and Lu, Xilong and Song, Heng and Gao, Fang",
    booktitle = "2025 IEEE International Conference on Robotics and Automation (ICRA)",
    organization = "IEEE",
    pages = "8234--8241",
    title = "Towards Robust Autonomous Driving: Conditional Multimodal Large Language Models for Fine-Grained Perception",
    year = "2025"
}

Huang et al., From Gaze to Movement: Predicting Visual Attention for Autonomous Driving Human-Machine Interaction based on Programmatic Imitation Learning, ICCV, 2025 | paper | code

Dataset(s): DATAD

@inproceedings{2025_ICCV_Huang,
    author = "Huang, Yexin and Lin, Yongbin and Yue, Lishengsa and Yao, Zhihong and Wang, Jie",
    booktitle = "Proceedings of the IEEE/CVF International Conference on Computer Vision",
    pages = "26146--26155",
    title = "From Gaze to Movement: Predicting Visual Attention for Autonomous Driving Human-Machine Interaction based on Programmatic Imitation Learning",
    year = "2025"
}

Zhao et al., SalM²: An Extremely Lightweight Saliency Mamba Model for Real-Time Cognitive Awareness of Driver Attention , AAAI, 2025 | paper | code

Dataset(s): BDD-A, DrFixD-rainy, TrafficSaliency

@inproceedings{2025_AAAI_Zhao,
    author = "Zhao, Chunyu and Mu, Wentao and Zhou, Xian and Liu, Wenbo and Yan, Fei and Deng, Tao",
    booktitle = "Proceedings of the AAAI Conference on Artificial Intelligence",
    number = "2",
    pages = "1647--1655",
    title = "SalM$^2$: An Extremely Lightweight Saliency Mamba Model for Real-Time Cognitive Awareness of Driver Attention",
    volume = "39",
    year = "2025"
}

Gupta et al., Object Importance Estimation Using Counterfactual Reasoning for Intelligent Driving, R-AL, 2024 | paper | code

Dataset(s): HOIST

@article{2024_R-AL_Gupta,
    author = "Gupta, Pranay and Biswas, Abhijat and Admoni, Henny and Held, David",
    journal = "IEEE Robotics and Automation Letters",
    publisher = "IEEE",
    title = "Object Importance Estimation using Counterfactual Reasoning for Intelligent Driving",
    year = "2024"
}

Kotseruba et al., SCOUT+: Towards Practical Task-Driven Drivers’ Gaze Prediction, IV, 2024 | paper | code

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

@inproceedings{2024_IV_Kotseruba_2,
    author = "Kotseruba, Iuliia and Tsotsos, John K",
    booktitle = "Intelligent Vehicles Symposium (IV)",
    title = "{SCOUT+: Towards Practical Task-Driven Drivers' Gaze Prediction}",
    year = "2024"
}

Kotseruba et al., Data Limitations for Modeling Top-Down Effects on Drivers’ Attention, IV, 2024 | paper | code

Dataset(s): DR(eye)VE, BDD-A, MAAD, LBW, SCOUT

@inproceedings{2024_IV_Kotseruba_1,
    author = "Kotseruba, Iuliia and Tsotsos, John K",
    booktitle = "Intelligent Vehicles Symposium (IV)",
    title = "Data Limitations for Modeling Top-Down Effects on Drivers' Attention",
    year = "2024"
}

Kotseruba et al., Understanding and Modeling the Effects of Task and Context on Drivers’ Gaze Allocation, IV, 2024 | paper | code

Dataset(s): DR(eye)VE, SCOUT

@inproceedings{2024_IV_Kotseruba,
    author = "Kotseruba, Iuliia and Tsotsos, John K",
    booktitle = "2024 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1337--1344",
    title = "Understanding and modeling the effects of task and context on drivers’ gaze allocation",
    year = "2024"
}

Deng et al., Driving Visual Saliency Prediction of Dynamic Night Scenes via a Spatio-Temporal Dual-Encoder Network, Trans. ITS, 2023 | paper | code

Dataset(s): DrFixD-night, DR(eye)VE

@article{2023_T-ITS_Deng,
    author = "Deng, Tao and Jiang, Lianfang and Shi, Yi and Wu, Jiang and Wu, Zhangbi and Yan, Shun and Zhang, Xianshi and Yan, Hongmei",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Driving Visual Saliency Prediction of Dynamic Night Scenes via a Spatio-Temporal Dual-Encoder Network",
    year = "2023"
}

Bhagat et al., Driver Gaze Fixation and Pattern Analysis in Safety Critical Events, IV, 2023 | paper | code

Dataset(s): SHRP2

@inproceedings{2023_IV_Bhagat,
    author = "Bhagat, Hirva and Jain, Sandesh and Abbott, Lynn and Sonth, Akash and Sarkar, Abhijit",
    booktitle = "2023 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1--8",
    title = "Driver gaze fixation and pattern analysis in safety critical events",
    year = "2023"
}

Zhao et al., Gated Driver Attention Predictor, ITSC, 2023 | paper | code

Dataset(s): DADA-2000, BDD-A

@inproceedings{2023_ITSC_Zhao,
    author = "Zhao, Tianci and Bai, Xue and Fang, Jianwu and Xue, Jianru",
    booktitle = "2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)",
    organization = "IEEE",
    pages = "270--276",
    title = "Gated Driver Attention Predictor",
    year = "2023"
}

Zhu et al., Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding, ICCV, 2023 | paper | code

Dataset(s): DR(eye)VE, BDD-A, DADA-2000

@inproceedings{2023_ICCV_Zhu,
    author = "Zhu, Pengfei and Qi, Mengshi and Li, Xia and Li, Weijian and Ma, Huadong",
    booktitle = "Proceedings of the IEEE/CVF International Conference on Computer Vision",
    pages = "8558--8568",
    title = "Unsupervised self-driving attention prediction via uncertainty mining and knowledge embedding",
    year = "2023"
}

Li et al., Adaptive Short-Temporal Induced Aware Fusion Network for Predicting Attention Regions Like a Driver, Trans. ITS, 2022 | paper | code

Dataset(s): BDD-A, DADA-2000, TrafficSaliency

@article{2022_T-ITS_Li,
    author = "Li, Qiang and Liu, Chunsheng and Chang, Faliang and Li, Shuang and Liu, Hui and Liu, Zehao",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "10",
    pages = "18695--18706",
    publisher = "IEEE",
    title = "Adaptive short-temporal induced aware fusion network for predicting attention regions like a driver",
    volume = "23",
    year = "2022"
}

Fang et al., DADA: Driver Attention Prediction in Driving Accident Scenarios, Trans. ITS, 2021 | paper | code

Dataset(s): TrafficSaliency, DR(eye)VE, DADA-2000

@article{2022_T-ITS_Fang,
    author = "Fang, Jianwu and Yan, Dingxin and Qiao, Jiahuan and Xue, Jianru and Yu, Hongkai",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "6",
    pages = "4959--4971",
    publisher = "IEEE",
    title = "DADA: Driver attention prediction in driving accident scenarios",
    volume = "23",
    year = "2021"
}

Araluce et al., ARAGAN: A dRiver Attention estimation model based on conditional Generative Adversarial Network, IV, 2022 | paper | code

Dataset(s): BDD-A, DADA-2000

@inproceedings{2022_IV_Araluce,
    author = "Araluce, Javier and Bergasa, Luis M and Oca{\\textasciitilde n}a, Manuel and Barea, Rafael and L{\'o}pez-Guill{\'e}n, Elena and Revenga, Pedro",
    booktitle = "2022 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1066--1072",
    title = "ARAGAN: A dRiver Attention estimation model based on conditional Generative Adversarial Network",
    year = "2022"
}

Kasahara et al., Look Both Ways: Self-Supervising Driver Gaze Estimation and Road Scene Saliency, ECCV, 2022 | paper | code

Dataset(s): LBW

@inproceedings{2022_ECCV_Kasahara,
    author = "Kasahara, Isaac and Stent, Simon and Park, Hyun Soo",
    booktitle = "Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XIII",
    organization = "Springer",
    pages = "126--142",
    title = "Look Both Ways: Self-supervising Driver Gaze Estimation and Road Scene Saliency",
    year = "2022"
}

Gopinath et al., MAAD: A Model and Dataset for “Attended Awareness” in Driving, ICCVW, 2021 | paper | code

Dataset(s): MAAD

@inproceedings{2021_ICCVW_Gopinath,
    author = "Gopinath, Deepak and Rosman, Guy and Stent, Simon and Terahata, Katsuya and Fletcher, Luke and Argall, Brenna and Leonard, John",
    booktitle = "Proceedings of the IEEE/CVF International Conference on Computer Vision",
    pages = "3426--3436",
    title = {MAAD: A Model and Dataset for" Attended Awareness" in Driving},
    year = "2021"
}

Baee et al., MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning, ICCV, 2021 | paper | code

Dataset(s): Eyecar

@inproceedings{2021_ICCV_Baee,
    author = "Baee, Sonia and Pakdamanian, Erfan and Kim, Inki and Feng, Lu and Ordonez, Vicente and Barnes, Laura",
    booktitle = "ICCV",
    title = "MEDIRL: Predicting the visual attention of drivers via maximum entropy deep inverse reinforcement learning",
    year = "2021"
}

Deng et al., How Do Drivers Allocate Their Potential Attention? Driving Fixation Prediction via Convolutional Neural Networks, Trans. ITS, 2020 | paper | code

Dataset(s): TrafficSaliency

@article{2020_T-ITS_Deng,
    author = "Deng, Tao and Yan, Hongmei and Qin, Long and Ngo, Thuyen and Manjunath, BS",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "5",
    pages = "2146--2154",
    publisher = "IEEE",
    title = "{How do drivers allocate their potential attention? Driving fixation prediction via convolutional neural networks}",
    volume = "21",
    year = "2019"
}

Pal et al., “Looking at the right stuff” - Guided semantic-gaze for autonomous driving, CVPR, 2020 | paper | code

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

@inproceedings{2020_CVPR_Pal,
    author = "Pal, Anwesan and Mondal, Sayan and Christensen, Henrik I",
    booktitle = "CVPR",
    title = {{" Looking at the Right Stuff"-Guided Semantic-Gaze for Autonomous Driving}},
    year = "2020"
}

Palazzi et al., Predicting the Driver’s Focus of Attention: the DR(eye)VE Project, PAMI, 2018 | paper | code

Dataset(s): DR(eye)VE

@article{2018_PAMI_Palazzi,
    author = "Palazzi, Andrea and Abati, Davide and Solera, Francesco and Cucchiara, Rita and others",
    journal = "IEEE TPAMI",
    number = "7",
    pages = "1720--1733",
    title = "{Predicting the Driver's Focus of Attention: the DR (eye) VE Project}",
    volume = "41",
    year = "2018"
}

Xia et al., Predicting Driver Attention in Critical Situations, ACCV, 2018 | paper | code

Dataset(s): BDD-A

@inproceedings{2018_ACCV_Xia,
    author = "Xia, Ye and Zhang, Danqing and Kim, Jinkyu and Nakayama, Ken and Zipser, Karl and Whitney, David",
    booktitle = "ACCV",
    title = "Predicting driver attention in critical situations",
    year = "2018"
}

Ohn-Bar et al., Are all objects equal? Deep spatio-temporal importance prediction in driving videos, Pattern Recognition, 2017 | paper | code

Dataset(s): KITTI

@article{2017_PR_Ohn-Bar,
    author = "Ohn-Bar, Eshed and Trivedi, Mohan Manubhai",
    journal = "Pattern Recognition",
    pages = "425--436",
    title = "Are all objects equal? Deep spatio-temporal importance prediction in driving videos",
    volume = "64",
    year = "2017"
}

Palazzi et al., Learning Where to Attend Like a Human Driver, IV, 2017 | paper | code

Dataset(s): DR(eye)VE

@inproceedings{2017_IV_Palazzi,
    author = "Palazzi, Andrea and Solera, Francesco and Calderara, Simone and Alletto, Stefano and Cucchiara, Rita",
    booktitle = "IV",
    title = "Learning where to attend like a human driver",
    year = "2017"
}

Deng et al., Where Does the Driver Look? Top-Down-Based Saliency Detection in a Traffic Driving Environment, Trans. ITS, 2016 | paper | code

Dataset(s): private

@article{2016_T-ITS_Deng,
    author = "Deng, Tao and Yang, Kaifu and Li, Yongjie and Yan, Hongmei",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "7",
    pages = "2051--2062",
    publisher = "IEEE",
    title = "Where does the driver look? Top-down-based saliency detection in a traffic driving environment",
    volume = "17",
    year = "2016"
}

Johnson et al., Predicting human visuomotor behaviour in a driving task, Philosophical Transactions of the Royal Society: B, 2013 | paper | code

Dataset(s): private

@article{2013_RSTB_Johnson,
    author = "Johnson, Leif and Sullivan, Brian and Hayhoe, Mary and Ballard, Dana",
    journal = "Philosophical Transactions of the Royal Society B: Biological Sciences",
    number = "1636",
    pages = "20130044",
    title = "Predicting human visuomotor behaviour in a driving task",
    volume = "369",
    year = "2014"
}

Borji et al., Probabilistic Learning of Task-Specific Visual Attention, CVPR, 2012 | paper | code

Dataset(s): 3DDS

@inproceedings{2012_CVPR_Borji,
    author = "Borji, Ali and Sihite, Dicky N and Itti, Laurent",
    booktitle = "CVPR",
    title = "Probabilistic learning of task-specific visual attention",
    year = "2012"
}

In-vehicle gaze

Tamura et al., Cognitive Distraction Detection Using Gaze and Pupil with an Interpretable Approach, IV, 2025 | paper | code

Dataset(s): private

@inproceedings{2025_IV_Tamura,
    author = "Tamura, Kimimasa and Stent, Simon and Gideon, John and Shintani, Kohei and Rosman, Guy",
    booktitle = "2025 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1305--1312",
    title = "Cognitive Distraction Detection Using Gaze and Pupil with an Interpretable Approach",
    year = "2025"
}

Cheng et al., What Do You See in Vehicle? Comprehensive Vision Solution for In-Vehicle Gaze Estimation, CVPR, 2024 | paper | code

Dataset(s): IVGaze

@inproceedings{2024_CVPR_Cheng,
    author = "Cheng, Yihua and Zhu, Yaning and Wang, Zongji and Hao, Hongquan and Liu, Yongwei and Cheng, Shiqing and Wang, Xi and Chang, Hyung Jin",
    booktitle = "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition",
    pages = "1556--1565",
    title = "What Do You See in Vehicle? Comprehensive Vision Solution for In-Vehicle Gaze Estimation",
    year = "2024"
}

Rangesh et al., Driver Gaze Estimation in the Real World: Overcoming the Eyeglass Challenge, IV, 2020 | paper | code

Dataset(s): LISA v3

@inproceedings{2020_IV_Rangesh,
    author = "Rangesh, Akshay and Zhang, Bowen and Trivedi, Mohan M",
    booktitle = "IV",
    title = "Driver gaze estimation in the real world: Overcoming the eyeglass challenge",
    year = "2020"
}

Stappen et al., X-AWARE: ConteXt-AWARE Human-Environment Attention Fusion for Driver Gaze Prediction in the Wild, ICMI, 2020 | paper | code

Dataset(s): DGW

@inproceedings{2020_ICMI_Stappen,
    author = {Stappen, Lukas and Rizos, Georgios and Schuller, Bj{\"o}rn},
    booktitle = "ICMI",
    title = "X-AWARE: ConteXt-AWARE Human-Environment Attention Fusion for Driver Gaze Prediction in the Wild",
    year = "2020"
}

Jokinen et al., Multitasking in Driving as Optimal Adaptation Under Uncertainty, Human Factors, 2020 | paper | code

Dataset(s): private

@article{2020_HumanFactors_Jokinen,
    author = "Jokinen, Jussi PP and Kujala, Tuomo and Oulasvirta, Antti",
    journal = "Human factors",
    number = "8",
    pages = "1324--1341",
    publisher = "Sage Publications Sage CA: Los Angeles, CA",
    title = "Multitasking in driving as optimal adaptation under uncertainty",
    volume = "63",
    year = "2021"
}

Distraction detection

Tamura et al., Cognitive Distraction Detection Using Gaze and Pupil with an Interpretable Approach, IV, 2025 | paper | code

Dataset(s): private

@inproceedings{2025_IV_Tamura,
    author = "Tamura, Kimimasa and Stent, Simon and Gideon, John and Shintani, Kohei and Rosman, Guy",
    booktitle = "2025 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1305--1312",
    title = "Cognitive Distraction Detection Using Gaze and Pupil with an Interpretable Approach",
    year = "2025"
}

Yang et al., Quantitative Identification of Driver Distraction: A Weakly Supervised Contrastive Learning Approach, Trans. ITS, 2024 | paper | code

Dataset(s): 3MDAD, AUCD2, SAM-DD

@article{2024_T-ITS_Yang,
    author = "Yang, Haohan and Liu, Haochen and Hu, Zhongxu and Nguyen, Anh-Tu and Guerra, Thierry-Marie and Lv, Chen",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Quantitative identification of driver distraction: A weakly supervised contrastive learning approach",
    year = "2023"
}

Li et al., A Lightweight and Efficient Distracted Driver Detection Model Fusing Convolutional Neural Network and Vision Transformer, Trans. ITS, 2024 | paper | code

Dataset(s): SFDDD, 100-Driver

@article{2024_T-ITS_Li_1,
    author = "Li, Zhao and Zhao, Xia and Wu, Fuwei and Chen, Dan and Wang, Chang",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "A Lightweight and Efficient Distracted Driver Detection Model Fusing Convolutional Neural Network and Vision Transformer",
    year = "2024"
}

Hasan et al., Vision-Language Models Can Identify Distracted Driver Behavior From Naturalistic Videos, Trans. ITS, 2024 | paper | code

Dataset(s): DMD, SAM-DD, SFDDD, SynDD1

@article{2024_T-ITS_Hasan,
    author = "Hasan, Md Zahid and Chen, Jiajing and Wang, Jiyang and Rahman, Mohammed Shaiqur and Joshi, Ameya and Velipasalar, Senem and Hegde, Chinmay and Sharma, Anuj and Sarkar, Soumik",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Vision-language models can identify distracted driver behavior from naturalistic videos",
    year = "2024"
}

Ma et al., ViT-DD: Multi-Task Vision Transformer for Semi-Supervised Driver Distraction Detection, IV, 2024 | paper | code

Dataset(s): SFDDD, AUCD2

@inproceedings{2024_IV_Ma,
    author = "Ma, Yunsheng and Wang, Ziran",
    booktitle = "2024 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "417--423",
    title = "Vit-dd: Multi-task vision transformer for semi-supervised driver distraction detection",
    year = "2024"
}

Sonth et al., Explainable Driver Activity Recognition Using Video Transformer in Highly Automated Vehicle, IV, 2023 | paper | code

Dataset(s): VTTIMLP01, SHRP2

@inproceedings{2023_IV_Sonth,
    author = "Sonth, Akash and Sarkar, Abhijit and Bhagat, Hirva and Abbott, Lynn",
    booktitle = "2023 IEEE Intelligent Vehicles Symposium (IV)",
    organization = "IEEE",
    pages = "1--8",
    title = "Explainable Driver Activity Recognition Using Video Transformer in Highly Automated Vehicle",
    year = "2023"
}

Zhou et al., Multi View Action Recognition for Distracted Driver Behavior Localization, CVPRW, 2023 | paper | code

Dataset(s): AI CITY NDAR

@inproceedings{2023_CVPRW_Zhou,
    author = "Zhou, Wei and Qian, Yinlong and Jie, Zequn and Ma, Lin",
    booktitle = "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition",
    pages = "5375--5380",
    title = "Multi view action recognition for distracted driver behavior localization",
    year = "2023"
}

Drowsiness detection

Liu et al., MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perception, CVPR, 2025 | paper | code

Dataset(s): AIDE

@inproceedings{2025_CVPR_Liu,
    author = "Liu, Wenzhuo and Wang, Wenshuo and Qiao, Yicheng and Guo, Qiannan and Zhu, Jiayin and Li, Pengfei and Chen, Zilong and Yang, Huiming and Li, Zhiwei and Wang, Lening and others",
    booktitle = "Proceedings of the Computer Vision and Pattern Recognition Conference",
    pages = "6864--6874",
    title = "Mmtl-uniad: A unified framework for multimodal and multi-task learning in assistive driving perception",
    year = "2025"
}

Driver awareness

Angkan et al., Multimodal Brain–Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines, Trans. ITS, 2024 | paper | code

Dataset(s): CL-Drive

@article{2024_T-ITS_Angkan,
    author = "Angkan, Prithila and Behinaein, Behnam and Mahmud, Zunayed and Bhatti, Anubhav and Rodenburg, Dirk and Hungler, Paul and Etemad, Ali",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    publisher = "IEEE",
    title = "Multimodal Brain--Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines",
    year = "2024"
}

Liao et al., Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments, ICRA, 2024 | paper | code

Dataset(s): NGSIM, HighD, MoCAD

@inproceedings{2024_ICRA_Liao,
    author = "Liao, Haicheng and Liu, Shangqian and Li, Yongkang and Li, Zhenning and Wang, Chengyue and Li, Yunjian and Li, Shengbo Eben and Xu, Chengzhong",
    booktitle = "2024 IEEE International Conference on Robotics and Automation (ICRA)",
    organization = "IEEE",
    pages = "14212--14219",
    title = "Human observation-inspired trajectory prediction for autonomous driving in mixed-autonomy traffic environments",
    year = "2024"
}

Zhou et al., Using Eye-Tracking Data to Predict Situation Awareness in Real Time During Takeover Transitions in Conditionally Automated Driving, Trans. ITS, 2021 | paper | code

Dataset(s): private

@article{2022_T-ITS_Zhou,
    author = "Zhou, Feng and Yang, X Jessie and de Winter, Joost CF",
    journal = "IEEE Transactions on Intelligent Transportation Systems",
    number = "3",
    pages = "2284--2295",
    publisher = "IEEE",
    title = "Using eye-tracking data to predict situation awareness in real time during takeover transitions in conditionally automated driving",
    volume = "23",
    year = "2021"
}

Self-driving

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"
}

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"
}

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"
}

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"
}

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"
}

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"
}