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International Conference on Smart Cities and Green ICT Systems

International Conference on Vehicle Technology and Intelligent Transport Systems

SMARTGREENS 2018, VEHITS 2018: Smart Cities, Green Technologies and Intelligent Transport Systems pp 202–226Cite as

Static Environment Perception Based on High-Resolution Automotive Radars

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 992))

Abstract

High-resolution radar sensors have the capability to perceive the surroundings around the vehicle very exactly by detecting thousands of reflection points per measurement cycle. To model the static environment with these detection points, a novel approach of occupancy grid mapping is developed in this paper. The reflection amplitudes of all data points are compensated, normalized, and then converted to a detection probability value that is based on a predefined radar sensor model. According to the movement of the test vehicle, the a posteriori occupancy probability after several measurement cycles is computed to build the occupancy grid map. Thereafter this occupancy grid map is transformed into a binary grid map, where the grid cells, with an obstacle present, are defined as occupied. Through the Connected-Component Labelling algorithm, these occupied grid cells are then clustered and all the outliers with only a few grid cells are eliminated. Then, the boundaries of the clustered, occupied grid cells are recognized by the Moore-Neighbor Tracing algorithm. Based on these boundaries, the free space of an interval-based model is determined by using the Bresenham’s line algorithm. The occupancy grid map and the free space detection results elaborated in this paper from the recorded radar measurements show a perfect match with the real road scenarios.

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References

  1. Meinl, F., Stolz, M., Kunert, M., Blume, H.: An experimental high performance radar system for highly automated driving. In: IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), Nagoya, pp. 71–74. IEEE (2017). https://doi.org/10.1109/icmim.2017.7918859

  2. Moravec, H., Elfes, A.: High resolution maps from wide angle sonar. In: Proceedings of IEEE International Conference on Robotics and Automation, St. Louis, pp. 116–121. IEEE (1985). https://doi.org/10.1109/robot.1985.1087316

  3. Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Comput. 22(6), 46–57 (1989). https://doi.org/10.1109/2.30720

    Article  Google Scholar 

  4. Mouhagir, H., Cherfaoui, V., Talj, R., Aioun, F., Guillemard, F.: Using evidential occupancy grid for vehicle trajectory planning under uncertainty with tentacles. In: IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, pp. 1–7. IEEE (2017). https://doi.org/10.1109/itsc.2017.8317808

  5. Weiss, T., Schiele, B., Dietmayer, K.: Robust driving path detection in urban and highway scenarios using a laser scanner and online occupancy grids. In: IEEE Intelligent Vehicles Symposium (IV), Istanbul, pp. 184–189. IEEE (2007). https://doi.org/10.1109/ivs.2007.4290112

  6. Badino, H., Mester, R., Vaudrey, T., Franke, U., Daimler AG: Stereo-Based free space computation in complex traffic scenarios. In: IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe, pp. 189–192. IEEE (2008). https://doi.org/10.1109/ssiai.2008.4512317

  7. Nuss, D.: A random finite set approach for dynamic occupancy grid maps. Ph.D. thesis, University of Ulm, Ulm (2017). https://doi.org/10.18725/oparu-4361

  8. Degerman, J., Pernstål, T., Alenljung, K.: 3D occupancy grid mapping using statistical radar models. In: IEEE Intelligent Vehicles Symposium (IV), Gothenburg, pp. 902–908. IEEE (2016). https://doi.org/10.1109/ivs.2016.7535495

  9. Clarke, B., Worrall, S., Brooker, G., Nebot, E.: Sensor modelling for radar-based occupancy mapping. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, pp. 3047–3054. IEEE (2012). https://doi.org/10.1109/iros.2012.6386255

  10. Werber, K., et al.: Automotive radar gridmap representations. In: IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), Heidelberg, pp. 1–4. IEEE (2015). https://doi.org/10.1109/icmim.2015.7117922

  11. Lombacher, J., Laudt, K., Hahn, M., Dickmann, J., Wöhler, C.: Semantic radar grids. In: IEEE Intelligent Vehicles Symposium (IV), Los Angeles, pp. 1170–1175. IEEE (2017). https://doi.org/10.1109/ivs.2017.7995871

  12. Homm, F., Kaempchen, N., Ota, J., Burschka, D.: Efficient occupancy grid computation on the GPU with LiDAR and radar for road boundary detection. In: IEEE Intelligent Vehicles Symposium (IV), San Diego, pp. 1006–1013. IEEE (2010). https://doi.org/10.1109/ivs.2010.5548091

  13. Badino, H., Franke, U., Mester, R.: Free space computation using stochastic occupancy grids and dynamic programming. In: International Conference on Computer Vision (ICCV), Workshop on Dynamical Vision, Rio de Janeiro. IEEE (2007)

    Google Scholar 

  14. Andrew, B., Isard, M. (eds.): Active Contours: The Application of Techniques From Graphics Vision Control Theory and Statistics to Visual Tracking of Shapes in Motion. Springer, London (1998). https://doi.org/10.1007/978-1-4471-1555-7

    Book  Google Scholar 

  15. Konrad, M., Szczot, M., Dietmayer, K.: Road course estimation in occupancy grids. In: IEEE Intelligent Vehicles Symposium (IV), San Diego, pp. 412–417. IEEE (2010). https://doi.org/10.1109/ivs.2010.5548041

  16. Lundquist, C., Schön, T.B., Orguner, U.: Estimation of the free space in front of a moving vehicle. In: Proceedings of the SAE World Congress and Exhibition. Detroit, SAE International (2009)

    Google Scholar 

  17. Schreier, M., Willert, V., Adamy, J.: Compact representation of dynamic driving environments for ADAS by parametric free space and dynamic object maps. IEEE Trans. Intell. Transp. Syst. 17(2), 367–384 (2016). https://doi.org/10.1109/TITS.2015.2472965

    Article  Google Scholar 

  18. Li, M.: High-Resolution radar based environment perception and maneuver planning. In: CTI Symposium on Automated Driving, Future Mobility and Digitalization (ADFD). Hannover, Euroforum (2017)

    Google Scholar 

  19. Stellet, J.E., Straub, F., Schumacher, J., Branz, W., Zöllner, J.M.: Estimating the process noise variance for vehicle motion models. In: IEEE 18th International Conference on Intelligent Transportation Systems (ITSC), Las Palmas, pp. 1512–1519. IEEE (2015). https://doi.org/10.1109/itsc.2015.212

  20. Li, M., Feng, Z., Stolz, M., Kunert, M., Henze, R., Küçükay F.: High resolution radar-based occupancy grid mapping and free space detection. In: Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS), Funchal, pp. 70–81. SciTePress (2018). https://doi.org/10.5220/0006667300700081

  21. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982). https://doi.org/10.1109/TIT.1982.1056489

    Article  MathSciNet  MATH  Google Scholar 

  22. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, pp. 226–331. AAAI Press (1996)

    Google Scholar 

  23. Rosenfeld, A., Pfaltz, J.L.: Sequential operations in digital picture processing. J. ACM 13(4), 471–494 (1999). https://doi.org/10.1145/321356.321357

    Article  MATH  Google Scholar 

  24. He, L., Chao, Y., Suzuki, K.: A run-based two-scan labeling algorithm. IEEE Trans. Image Process. 17(5), 749–756 (2008). https://doi.org/10.1109/TIP.2008.919369

    Article  MathSciNet  Google Scholar 

  25. Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB. Pearson Prentice Hall, Lexington (2004)

    Google Scholar 

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Acknowledgements

This work has received funding from the European Community’s Eighth Framework Program (Horizon2020) under grant agreement no. 634149 for the PROSPECT project and funding from the German Federal Ministry for Economic Affairs and Energy (BMWi) for the iFUSE project. The PROSPECT and iFUSE consortium members express their gratitude for selecting and supporting these two projects.

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Authors and Affiliations

  1. Advanced Engineering Sensor Systems, Robert Bosch GmbH, Daimlerstr. 6, 71229, Leonberg, Germany

    Mingkang Li, Zhaofei Feng, Martin Stolz & Martin Kunert

  2. Institute of Automotive Engineering, Technische Universität Braunschweig, Hans-Sommer-Str. 4, 38106, Brunswick, Germany

    Mingkang Li, Roman Henze & Ferit Küçükay

Authors
  1. Mingkang Li
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  2. Zhaofei Feng
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  3. Martin Stolz
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  4. Martin Kunert
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  5. Roman Henze
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  6. Ferit Küçükay
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Corresponding author

Correspondence to Mingkang Li .

Editor information

Editors and Affiliations

  1. School of Business, Maynooth University, Maynooth, Ireland

    Brian Donnellan

  2. CT RDA SSI, Siemens AG, Munich, Bayern, Germany

    Cornel Klein

  3. Dublin City University, Dublin, Ireland

    Markus Helfert

  4. Ford Research and Advanced Engineer, Dearborn, MI, USA

    Oleg Gusikhin

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Li, M., Feng, Z., Stolz, M., Kunert, M., Henze, R., Küçükay, F. (2019). Static Environment Perception Based on High-Resolution Automotive Radars. In: Donnellan, B., Klein, C., Helfert, M., Gusikhin, O. (eds) Smart Cities, Green Technologies and Intelligent Transport Systems. SMARTGREENS VEHITS 2018 2018. Communications in Computer and Information Science, vol 992. Springer, Cham. https://doi.org/10.1007/978-3-030-26633-2_10

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  • DOI: https://doi.org/10.1007/978-3-030-26633-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26632-5

  • Online ISBN: 978-3-030-26633-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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