A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 7 Issue 2
Mar.  2020

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Chen Sun, Jean M. Uwabeza Vianney, Ying Li, Long Chen, Li Li, Fei-Yue Wang, Amir Khajepour and Dongpu Cao, "Proximity Based Automatic Data Annotation for Autonomous Driving," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 395-404, Mar. 2020. doi: 10.1109/JAS.2020.1003033
Citation: Chen Sun, Jean M. Uwabeza Vianney, Ying Li, Long Chen, Li Li, Fei-Yue Wang, Amir Khajepour and Dongpu Cao, "Proximity Based Automatic Data Annotation for Autonomous Driving," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 395-404, Mar. 2020. doi: 10.1109/JAS.2020.1003033

Proximity Based Automatic Data Annotation for Autonomous Driving

doi: 10.1109/JAS.2020.1003033
More Information
  • The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding. Today, most autonomous vehicles employ expensive high quality sensor-set such as light detection and ranging (LIDAR) and HD maps with high level annotations. In this paper, we propose a scalable and affordable data collection and annotation framework, image-to-map annotation proximity (I2MAP), for affordance learning in autonomous driving applications. We provide a new driving dataset using our proposed framework for driving scene affordance learning by calibrating the data samples with available tags from online database such as open street map (OSM). Our benchmark consists of 40 000 images with more than 40 affordance labels under various day time and weather even with very challenging heavy snow. We implemented sample advanced driver-assistance systems (ADAS) functions by training our data with neural networks (NN) and cross-validate the results on benchmarks like KITTI and BDD100K, which indicate the effectiveness of our framework and training models.

     

  • loading
  • [1]
    F.-Y. Wang, N.-N. Zheng, D. Cao, C. M. Martinez, L. Li, and T. Liu, “Parallel driving in cpss: a unified approach for transport automation and vehicle intelligence,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 577–587, 2017. doi: 10.1109/JAS.2017.7510598
    [2]
    C. Lv, D. Cao, Y. Zhao, D. J. Auger, M. Sullman, H. Wang, L. M. Dutka, L. Skrypchuk, and A. Mouzakitis, “Analysis of autopilot disengagements occurring during autonomous vehicle testing,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 58–68, 2017.
    [3]
    Y. Xing, C. Lv, L. Chen, H. Wang, H. Wang, D. Cao, E. Velenis, and F.-Y. Wang, “Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on acp-based parallel vision,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 3, pp. 645–661, 2018. doi: 10.1109/JAS.2018.7511063
    [4]
    B. Jähne and H. Haußecker, Computer Vision and Applications: A Guide for Students and Practitioners, Elsevier, 2000.
    [5]
    S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Malaysia; Pearson Education Limited, 2016.
    [6]
    A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: the kitti dataset,” The Int. J. Robotics Research, vol. 32, no. 11, pp. 1231–1237, 2013. doi: 10.1177/0278364913491297
    [7]
    Z. Chen, J. Zhang, and D. Tao, “Progressive Lidar adaptation for road detection,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 693–702, 2019. doi: 10.1109/JAS.2019.1911459
    [8]
    C. Chen, A. Seff, A. Kornhauser, and J. Xiao, “Deepdriving: learning affordance for direct perception in autonomous driving,” in Proc. IEEE Int. Conf. Computer Vision, 2015, pp. 2722–2730.
    [9]
    H. Guo, D. Cao, H. Chen, C. Lv, H. Wang, and S. Yang, “Vehicle dynamic state estimation: state of the art schemes and perspectives,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 418–431, 2018. doi: 10.1109/JAS.2017.7510811
    [10]
    V. Rausch, A. Hansen, E. Solowjow, C. Liu, E. Kreuzer, and J. K. Hedrick, “Learning a deep neural net policy for end-to-end control of autonomous vehicles,” in Proc. IEEE American Control Conf., 2017, pp. 4914–4919.
    [11]
    A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: an open urban driving simulator,” in Proc. 1st Annual Conf. Robot Learning, 2017, pp. 1–16.
    [12]
    S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: high-fidelity visual and physical simulation for autonomous vehicles,” in Field and Service Robotics. Springer, 2018, pp. 621–635.
    [13]
    S. Kato, S. Tokunaga, Y. Maruyama, S. Maeda, M. Hirabayashi, Y. Kitsukawa, A. Monrroy, T. Ando, Y. Fujii, and T. Azumi, “Autoware on board: enabling autonomous vehicles with embedded systems,” in Proc. ACM/IEEE 9th Int. Conf. Cyber-Physical Systems, 2018, pp. 287–296.
    [14]
    H. Xu, Y. Gao, F. Yu, and T. Darrell, “End-to-end learning of driving models from large-scale video datasets,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2017, pp. 3530–3538.
    [15]
    A. Seff and J. Xiao, “Learning from maps: visual common sense for autonomous driving,” arXiv Preprint arXiv: 1611.08583, 2016.
    [16]
    M. Haklay and P. Weber, “Open street map: user-generated street maps,” IEEE Pervas Comput., vol. 7, no. 4, pp. 12–18, 2008. doi: 10.1109/MPRV.2008.80
    [17]
    F. Yu, W. Xian, Y. Chen, F. Liu, M. Liao, V. Madhavan, and T. Darrell, “BDD100K: a diverse driving video database with scalable annotation tooling,” arXiv Preprint arXiv: 1805.04687, 2018.
    [18]
    M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2016, pp. 3213–3223.
    [19]
    S. Lee, J. Kim, J. Shin Yoon, S. Shin, O. Bailo, N. Kim, T.-H. Lee, H. Seok Hong, S.-H. Han, and I. So Kweon, “Vpgnet: vanishing point guided network for lane and road marking detection and recognition,” in Proc. IEEE Int. Conf. Computer Vision, 2017, pp. 1947–1955.
    [20]
    G. Li, Y. Yang, and X. Qu, “Deep learning approaches on pedestrian detection in hazy weather,” IEEE Trans. Industrial Electronics, 2019. [Online]. Avaliable: https://ieeexplore.ieee.org/document/8880634/
    [21]
    X. Huang, X. Cheng, Q. Geng, B. Cao, D. Zhou, P. Wang, Y. Lin, and R. Yang, “The apolloscape dataset for autonomous driving,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops, 2018, pp. 954–960.
    [22]
    A. Sauer, N. Savinov, and A. Geiger, “Conditional affordance learning for driving in urban environments,” arXiv Preprint arXiv: 1806.06498, 2018.
    [23]
    C. Sun, J. M. U. Vianney, and D. Cao, “Affordance learning in direct perception for autonomous driving,” arXiv Preprint arXiv: 1903.08746, 2019.
    [24]
    S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. Lawrence Zitnick, and D. Parikh, “VQA: visual question answering,” in Proc. IEEE Int. Conf. Computer Vision, 2015, pp. 2425–2433.
    [25]
    S. Ruder, “An overview of multi-task learning in deep neural networks,” arXiv Preprint arXiv: 1706.05098, 2017.
    [26]
    S. Rezaei and R. Sengupta, “Kalman filter-based integration of dgps and vehicle sensors for localization,” IEEE Trans. Control Systems Technology, vol. 15, no. 6, pp. 1080–1088, 2007. doi: 10.1109/TCST.2006.886439
    [27]
    S. E. Li, G. Li, J. Yu, C. Liu, B. Cheng, J. Wang, and K. Li, “Kalman filter-based tracking of moving objects using linear ultrasonic sensor array for road vehicles,” Mechanical Systems and Signal Processing, vol. 98, pp. 173–189, 2018. doi: 10.1016/j.ymssp.2017.04.041
    [28]
    S. Gao, Y. Hou, H. Dong, S. Stichel, and B. Ning, “High-speed trains automatic operation with protection constraints: a resilient nonlinear gainbased feedback control approach,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 992–999, 2019. doi: 10.1109/JAS.2019.1911582
    [29]
    J. Kong, M. Pfeiffer, G. Schildbach, and F. Borrelli, “Kinematic and dynamic vehicle models for autonomous driving control design,” in Proc. IEEE Intelligent Vehicles Symp. 2015, pp. 1094–1099.
    [30]
    K. T. Leung, J. F. Whidborne, D. Purdy, and P. Barber, “Road vehicle state estimation using low-cost GPS/INS,” Mechanical Systems and Signal Processing, vol. 25, no. 6, pp. 1988–2004, 2011. doi: 10.1016/j.ymssp.2010.08.003
    [31]
    H. Schafer, E. Santana, A. Haden, and R. Biasini, “A commute in data: the comma2k19 dataset,” arXiv Preprint arXiv: 1812.057522018, 2018.
    [32]
    S. Miura, L.-T. Hsu, F. Chen, and S. Kamijo, “GPS error correction with pseudorange evaluation using three-dimensional maps,” IEEE Trans. Intelligent Transportation Systems, vol. 16, no. 6, pp. 3104–3115, 2015. doi: 10.1109/TITS.2015.2432122
    [33]
    H. Sairo, D. Akopian, and J. Takala, “Weighted dilution of precision as quality measure in satellite positioning,” IEE Proceedings-Radar,Sonar and Navigation, vol. 150, no. 6, pp. 430–436, 2003. doi: 10.1049/ip-rsn:20031008
    [34]
    K. Czarnecki and R. Salay, “Towards a framework to manage perceptual uncertainty for safe automated driving,” in Proc. Int. Conf. Computer Safety, Reliability, and Security. Springer, 2018, pp. 439–445.
    [35]
    E. Herrera-Viedma, F. Herrera, and F. Chiclana, “A consensus model for multiperson decision making with different preference structures,” IEEE Trans. Systems,Man,and Cybernetics–Part A:Systems and Humans, vol. 32, no. 3, pp. 394–402, 2002. doi: 10.1109/TSMCA.2002.802821
    [36]
    P. M. Kebria, A. Khosravi, S. M. Salaken, and S. Nahavandi, “Deep imitation learning for autonomous vehicles based on convolutional neural networks,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 82–95, 2019.
    [37]
    Z. Wu, C. Shen, and A. Van Den Hengel, “Wider or deeper: revisiting the resnet model for visual recognition,” Pattern Recognition, vol. 90, pp. 119–133, 2019. doi: 10.1016/j.patcog.2019.01.006
    [38]
    J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and F. F. Li, “Imagenet: a large-scale hierarchical image database,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009, pp. 248–255.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)  / Tables(2)

    Article Metrics

    Article views (2650) PDF downloads(89) Cited by()

    Highlights

    • Affordable and scalable uncertainty-aware Image2Map annotation scheme for ADS.
    • Proximity-based label extraction with GNSS calibration and EKF based on kinematic vehicle model.
    • Present a scene understanding benchmark for multi-label learning collected in Canada winter.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return