Abstract
Current state-of-the-art imitation learning policies in autonomous driving, despite having good driving performance, do not consider the uncertainty in their predicted action. Using such an unleashed action without considering the degree of confidence in a black-box machine learning system can compromise safety and reliability in safety-critical applications such as autonomous driving. In this paper, we propose three different uncertainty-aware policies, to capture epistemic and aleatoric uncertainty over the continuous control commands. More specifically, we extend a state-of-the-art policy with three common uncertainty estimation methods: heteroscedastic aleatoric, MC-Dropout and Deep Ensembles. To provide accurate and calibrated uncertainty, we further combine our agents with isotonic regression, an existing calibration method in regression task. We benchmark and compare the driving performance of our uncertainty-aware agents in complex urban driving environments. Moreover, we evaluate the quality of predicted uncertainty before and after recalibration. The experimental results show that our Ensemble agent combined with isotonic regression not only provides accurate uncertainty for its predictions but also significantly outperforms the state-of-the-art baseline in driving performance.
Keywords
This research was supported by the German Federal Ministry for Education and Research (BMB+F) in the project REACT.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bojarski, M., et al.: End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 (2016)
Chen, C., Seff, A., Kornhauser, A., Xiao, J.: Deepdriving: learning affordance for direct perception in autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2722–2730 (2015). https://doi.org/10.1109/ICCV.2015.312
Chen, D., Zhou, B., Koltun, V., Krähenbühl, P.: Learning by cheating. In: Conference on Robot Learning, pp. 66–75. PMLR (2020)
Codevilla, F., Müller, M., López, A., Koltun, V., Dosovitskiy, A.: End-to-end driving via conditional imitation learning. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1–9. IEEE (2018). https://doi.org/10.1109/ICRA.2018.8460487
Codevilla, F., Santana, E., López, A.M., Gaidon, A.: Exploring the limitations of behavior cloning for autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9329–9338 (2019). https://doi.org/10.1109/iccv.2019.00942
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: an open urban driving simulator. arXiv preprint arXiv:1711.03938 (2017)
Feng, D., Rosenbaum, L., Dietmayer, K.: Towards safe autonomous driving: capture uncertainty in the deep neural network for lidar 3d vehicle detection. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 3266–3273. IEEE (2018). https://doi.org/10.1109/ITSC.2018.8569814
Feng, D., Rosenbaum, L., Glaeser, C., Timm, F., Dietmayer, K.: Can we trust you? On calibration of a probabilistic object detector for autonomous driving. arXiv preprint arXiv:1909.12358 (2019)
Feng, D., Rosenbaum, L., Timm, F., Dietmayer, K.: Leveraging heteroscedastic aleatoric uncertainties for robust real-time lidar 3D object detection. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 1280–1287. IEEE (2019). https://doi.org/10.1109/IVS.2019.8814046
Gal, Y.: Uncertainty in Deep Learning. University of Cambridge, Cambridge, vol. 1, p. 3 (2016)
Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050–1059 (2016)
Guafsson, F.K., Danelljan, M., Schon, T.B.: Evaluating scalable Bayesian deep learning methods for robust computer vision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 318–319 (2020). https://doi.org/10.1109/CVPRW50498.2020.00167
Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1321–1330. ICML 2017, JMLR (2017)
Harakeh, A., Smart, M., Waslander, S.L.: BayesOD: a Bayesian approach for uncertainty estimation in deep object detectors. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 87–93. IEEE (2020)
He, Y., Zhu, C., Wang, J., Savvides, M., Zhang, X.: Bounding box regression with uncertainty for accurate object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2888–2897 (2019). https://doi.org/10.1109/CVPR.2019.00300
Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, pp. 5574–5584 (2017)
Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7482–7491 (2018)
Kuleshov, V., Fenner, N., Ermon, S.: Accurate uncertainties for deep learning using calibrated regression. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, Sockholmsmässan, Stockholm, Sweden, vol. 80, pp. 2796–2804. PMLR, 10–15 July 2018. http://proceedings.mlr.press/v80/kuleshov18a.html
Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in Neural Information Processing Systems, pp. 6402–6413 (2017)
McAllister, R., et al.: Concrete problems for autonomous vehicle safety: Advantages of bayesian deep learning. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 4745–4753 (2017). https://doi.org/10.24963/ijcai.2017/661
Muller, U., Ben, J., Cosatto, E., Flepp, B., Cun, Y.L.: Off-road obstacle avoidance through end-to-end learning. In: Advances in Neural Information Processing Systems, pp. 739–746 (2006)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 8024–8035. Curran Associates, Inc. (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Platt, J., et al.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classifiers 10(3), 61–74 (1999)
Sauer, A., Savinov, N., Geiger, A.: Conditional affordance learning for driving in urban environments. In: Conference on Robot Learning, pp. 237–252 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Nozarian, F., Müller, C., Slusallek, P. (2021). Uncertainty Quantification and Calibration of Imitation Learning Policy in Autonomous Driving. In: Heintz, F., Milano, M., O'Sullivan, B. (eds) Trustworthy AI - Integrating Learning, Optimization and Reasoning. TAILOR 2020. Lecture Notes in Computer Science(), vol 12641. Springer, Cham. https://doi.org/10.1007/978-3-030-73959-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-73959-1_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73958-4
Online ISBN: 978-3-030-73959-1
eBook Packages: Computer ScienceComputer Science (R0)