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2023 | OriginalPaper | Buchkapitel

Online Perception Performance Estimation for Autonomous Driving Vehicles

verfasst von : Ziyu Qin, Zhao Zhang

Erschienen in: Proceedings of China SAE Congress 2022: Selected Papers

Verlag: Springer Nature Singapore

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Abstract

Accurate estimation on the perception performance is an essential prerequisite for autonomous driving safety. State-of-the-art perception models get excellent performance on existing well-labelled dataset, while suffering the lack of reliable confidence in varying scenarios. This paper is dedicated to investigate an online perception performance estimation, in particular, to calculate the precision rate of each detected object adaptively without manually labelled annotations. A parameterized relationship is constructed between the confidence output by perception models and the precision performance. And then the relationship can be online updated according to a robust multiple hypothesis tracking (MHT) system iteratively. The proposed method is applied on widely studied Mask-RCNN on the KITTI dataset, to get precision estimations accordant to the ground-truth in different scenarios, respectively.

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Vorheriges Kapitel ITGAN: An Interactive Trajectories Generative Adversarial Network Model for Automated Driving Scenario Generation

Nächstes Kapitel A Trade-off Design Approach for Integrating Cybersecurity, Safety, and Other Aspects of Intelligent Connected Vehicles

Wang, H., Zhou, L., Wang, L: Miss detection vs. false alarm: adversarial learning for small object segmentation in infrared images In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 8508–8517

Blackman, S., Popoli. R.: Design and Analysis of Modern Tracking System. Artech House (1999)

Ma, X., Ouyang, W., Simonelli, A., et al.: 3D Object detection from images for autonomous driving: a survey. arXiv:2202.02980v1

Amersfoort, J.V., Smith, L., Teh, Y.W., et al.: Uncertainty estimation using a single deep deterministic neural network. arXiv:2003.02037v2

J. Gawlikowski, C.R.N. Tassi, M. Ali, et al. A Survey of Uncertainty in Deep Neural Networks. arXiv:2107.03342v1

Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. arXiv:1506.02640v5

Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Conference on Computer Vision and Pattern Recognition 2014, 580–587 (2014)

He, K., Zhang, X., Ren, S., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)

Zhou, Y., Tuzel. O.: VoxelNet: end-to-end learning for point cloud based 3D object detection. arXiv:1711.06396v1

10.

E.E. Aksoy, S. Baci, S. Cavdar. SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving. arXiv:1909.08291v1

11.

Qi, C.R., Su, H., Mo, K., et al.: PointNet: deep learning on point sets for 3D classification and Segmentation. arXiv:1612.00593v2

12.

Chen, X., Ma, H., Wan, J., et al.: Multi-view 3D object detection network for autonomous driving. arXiv:1611.07759v3

13.

C.R. Qi, W. Liu, C. Wu, et al.: Frustum PointNets for 3D object detection from RGB-D data. arXiv:1711.08488v2

14.

Weng, X., Kitani, K.: A baseline for 3D multi-object tracking. arXiv:1907.03961v3

15.

Monteiro, G., Premebida, C., Peixoto, P., et al.: Tracking and classification of dynamic obstacles using laser range finder and vision. In: IROS Workshop on Safe Navigation in Opern environments (2006)

16.

Kim, C., Li, F., Ciptadi, A., et al.: Multiple hypothesis tracking revisited. IEEE International Conference on Computer Vision (ICCV) 2015, 4696–4704 (2015)CrossRef

17.

Wald, A.: Sequential Analysis. Wiley and Sons, New York (1947)

18.

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)

19.

Feng, D., Cao, Y., Rosenbaum, L., et al.: Leveraging uncertainties for deep multi-modal object detection in autonomous driving. arXiv:2002.00216v1

20.

Papageorgiou, D.J., Salpukas, M.: The maximum weight independent set problem for data association in multiple hypothesis tracking. In: Optimization and Cooperative Control Strategies, pp. 235–255. https://doi.org/10.1007/978-3-540-88063-9_15

21.

Elo, A.E.: The rating of chess players, past and present. Acta Paediatrica 32(3–4), 201–217 (1978

Titel: Online Perception Performance Estimation for Autonomous Driving Vehicles
verfasst von: Ziyu Qin
Zhao Zhang
Verlag: Springer Nature Singapore
Buch: Proceedings of China SAE Congress 2022: Selected Papers
Print ISBN: 978-981-9913-64-0

Electronic ISBN: 978-981-9913-65-7

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-981-99-1365-7_42

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