Abstract
Human drivers employ their attentional systems during driving to focus on critical items and make judgments. Because gaze data can indicate human attention, collecting and analyzing gaze data has emerged in recent years to improve autonomous driving technologies. In safety-critical situations, it is important to predict not only where the driver focuses his attention but also on which objects. In this work, we propose DAP, a novel framework for driver attention prediction that bridges the attention prediction gap between pixels and objects. The DAP Framework is evaluated on the Berkeley DeepDrive Attention (BDD-A) dataset. DAP achieves state-of-the-art performance in both pixel-level and object-level attention prediction, especially improving object detection accuracy from 78 to 90%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gou, C., Zhou, Y., Li, D.: Driver attention prediction based on convolution and transformers. J. Supercomput. 78(6), 8268–8284 (2022)
Rong, Y., et al.: Where and what: driver attention-based object detection. In: Proceedings of the ACM on Human-Computer Interaction 6. ETRA, pp. 1–22 (2022)
Su, Y., et al.: A unified transformer framework for group-based segmentation: co-segmentation, co-saliency detection and video salient object detection (2022). arXiv:2203.04708
Fang, J., et al.: DADA: driver attention prediction in driving accident scenarios. In: IEEE Transactions on Intelligent Transportation Systems (2021)
Siddique, N., et al.: U-net and its variants for medical image segmentation: a review of theory and applications. IEEE Access 9, 82031–82057 (2021)
Pal, A., Mondal, S., Christensen, H.I.: Looking at the right stuff-guided semantic-gaze for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
Li, C., Chan, S.H., Chen, Y.T.: Who make drivers stop? towards driver-centric risk assessment: risk object identification via causal inference. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2020)
Dosovitskiy, A., et al.: An image is worth 16 \(\times \) 16 words: transformers for image recognition at scale (2020). arXiv:2010.11929
Kang, Y., Yin, H., Berger, C.: Test your self-driving algorithm: an overview of publicly available driving datasets and virtual testing environments. IEEE Trans. Intell. Veh. 4(2), 171–185 (2019)
Guo, J., Kurup, U., Shah, M.: Is it safe to drive? an overview of factors, metrics, and datasets for driveability assessment in autonomous driving. IEEE Trans. Intell. Transp. Syst. 21(8), 3135–3151 (2019)
Deng, T., et al.: How do drivers allocate their potential attention? driving fixation prediction via convolutional neural networks. In: IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 5, pp. 2146–2154 (2019)
Liu, C., et al.: A gaze model improves autonomous driving. In: Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications (2019)
Fang, J., et al.: Dada-2000: can driving accident be predicted by driver attention analyzed by a benchmark. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE (2019)
Fang, J., et al.: Dada: a large-scale benchmark and model for driver attention prediction in accidental scenarios (2019). arXiv:1912.12148
Xia, Y., et al.: Predicting driver attention in critical situations. In: Asian Conference on Computer Vision. Springer, Cham (2018)
Palazzi, A., et al.: Predicting the driver’s focus of attention: the DR (eye) VE project. In: IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1720–1733 (2018)
Tawari, A., Mallela, P., Martin, S.: Learning to attend to salient targets in driving videos using fully convolutional RNN. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE (2018)
Liu, N., Han, J., Yang, M.H.: PiCANet: Learning pixel-wise contextual attention for saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Yin, H., Berger, C.: When to use what data set for your self-driving car algorithm: an overview of publicly available driving datasets. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE (2017)
Tawari, A., Kang, B.: A computational framework for driver’s visual attention using a fully convolutional architecture. In: 2017 IEEE Intelligent Vehicles Symposium (IV). IEEE (2017)
Palazzi, A., et al.: Learning where to attend like a human driver. In: 2017 IEEE Intelligent Vehicles Symposium (IV). IEEE (2017)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Alletto, S., et al.: DR (eye) VE: a dataset for attention-based tasks with applications to autonomous and assisted driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2016)
Cornia, M., et al.: A deep multi-level network for saliency prediction. In: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham (2015)
Huang, X., et al.: Salicon: reducing the semantic gap in saliency prediction by adapting deep neural networks. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, vol. 19 (2006)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)
Jocher, G., Stoken, A., Borovec, J., Chaurasia, A., Xie, T., Changyu, L., Abhiram, V., Hogan, A., Hajek, J., Diaconu, L., Kwon, Y., Defretin, Y., Lohia, A.: Laughing, tkianai, yxNONG, lorenzomammana, AlexWang1900, Marc, oleg, wanghaoyang0106, ml5ah, Ben Milanko, Benjamin Fineran, Daniel Khromov, Ding Yiwei. Durgesh, and Francisco Ingham, Doug, NanoCode012 ultralytics/yolov5: v5.0 - YOLOv5-P6 1280 models (2021). https://doi.org/10.5281/zenodo.4679653
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kamel, A., Sobh, I., Al-Atabany, W. (2024). DAP: A Framework for Driver Attention Prediction. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 824. Springer, Cham. https://doi.org/10.1007/978-3-031-47715-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-47715-7_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47714-0
Online ISBN: 978-3-031-47715-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)