Road structure classification through artificial neural network for automotive radar systems

Road structure classification through artificial neural network for automotive radar systems

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Radar, Sonar & Navigation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study proposes an artificial neural network-based method to classify road structures for automotive radar systems. Generally, road structures generate unwanted echoes called clutter, which degrades the performance of target detection, and may cause critical errors in autonomous driving modes. However, recognition and classification of each individual structure type is very difficult, because there are various types of structures made by different materials which cause ambiguity in the recognition and classification process. To deal with these classification ambiguities, the authors propose an artificial neural network-based recognition and classification method. Road structures are classified by applying artificial neural network to the frequency domain-received signals of automotive radar systems. Once the neural network has been trained with the received signals, it is used to determine the type of road structure with instantaneous received signals. The classification performance was evaluated by experimenting with the number of antenna elements and radar snapshots. In addition, to find the most suitable artificial neural network structure, the authors experimented with the number of nodes and layers. After completing a period of deep learning using actual experimental data, the total classification accuracy was about 95%.


    1. 1)
      • 1. BIS Research: ‘Global automotive sensor market demand, supply and opportunities: estimation and forecast of (2015–2022)’ (BIS Research, Fremont, CA, USA, 2015), pp. 148.
    2. 2)
      • 2. Skolnik, M.I.: ‘Introduction to radar systems’ (McGraw-Hill, New York, NY, USA, 2001, 3rd edn.).
    3. 3)
      • 3. Jones, W.D.: ‘Keeping cars from crashing’, IEEE Spectr., 2001, 38, (9), pp. 4045.
    4. 4)
      • 4. Eriksson, L., Broden, S.: ‘High performance automotive radar’, Microw. J., 1996, 49, pp. 24238.
    5. 5)
      • 5. Rohling, H.: ‘A 77 GHz automotive radar system for AICC applications’. Proc. Int. Conf. Microwaves and Radar (MIKON98), Krakow, Poland, 1998.
    6. 6)
      • 6. Rohling, H., Meinecke, M.-M.: ‘Waveform design principles for automotive radar systems’. Proc. CIE Int. Conf. Radar, Beijing, China, 2001, pp. 14.
    7. 7)
      • 7. Schneider, M.: ‘Automotive radar – status and trends’. Proc. German Microwave Conf., Ulm, Germany, April 2005, pp. 351354.
    8. 8)
      • 8. Nagy, L.L.: ‘Electromagnetic reflectivity characteristics of road surfaces’, IEEE Trans. Veh. Technol., 1974, IT-23, (4), pp. 117124.
    9. 9)
      • 9. Schneider, R., Didascalou, D., Wiesbeck, W.: ‘Impact of road surfaces on millimeter-wave propagation’, IEEE Trans. Veh. Technol., 2000, 49, (4), pp. 13141320.
    10. 10)
      • 10. Pathak, P.H., Burnside, W.D., Marhefka, R.J.: ‘A uniform GTD analysis of the diffraction of eletromagnetic waves by a smooth convex surface’, IEEE Trans. Ant. Propag., 1980, AP-28, (5), pp. 631642.
    11. 11)
      • 11. Matsunami, I., Kajiwara, A.: ‘Clutter suppression scheme for vehicle radar’. IEEE Radio and Wireless Symp., LA, USA, January 2010, pp. 320323.
    12. 12)
      • 12. Ma, Y.-Z., Cui, C., Kim, B.-S., et al: ‘Road clutter spectrum of BSD FMCW automotive radar’. IEEE European Radar Conf. (EuRAD), Paris, France, September 2015, pp. 109112.
    13. 13)
      • 13. Lee, J.-E., Lim, H.-S., Jeong, S.-H., et al: ‘Enhanced iron-tunnel recognition for automotive radars’, IEEE Trans. Veh. Technol., 2016, 65, (6), pp. 44124418.
    14. 14)
      • 14. Lee, H.-B., Lee, J.-E., Lim, H.-S., et al: ‘Clutter suppression method of iron tunnel using cepstral analysis for automotive radars’, IEICE Trans. Commun., 2017, E100-B, (2), pp. 400406.
    15. 15)
      • 15. Lee, J.-E., Lim, H.-S., Jeong, S.-H., et al: ‘Harmonic clutter recognition and suppression for automotive radar sensors’, Int. J. Distrib. Sens. Netw., 2017, 13, (9), pp. 111.
    16. 16)
      • 16. Takagi, K., Morikawa, K., Ogawa, T., et al: ‘Road environment recognition using on-vehicle LIDAR’. IEEE Intelligent Vehicles Symp., Tokyo, Japan, June 2006, pp. 120125.
    17. 17)
      • 17. Broggi, A., Cerri, P., Medici, P., et al: ‘Real time road signs recognition’. IEEE Intelligent Vehicles Symp., Istanbul, Turkey, June 2007, pp. 981986.
    18. 18)
      • 18. Zhou, L., Deng, Z.: ‘LIDAR and vision-based real-time traffic sign detection and recognition algorithm for intelligent vehicle’. IEEE Int. Conf. on Intelligent Transportation Systems (ITSC), Qingdao, China, October 2014, pp. 578583.
    19. 19)
      • 19. Guan, H., Li, J., Yu, Y., et al: ‘Using mobile LiDAR data for rapidly updating road markings’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (5), pp. 24572466.
    20. 20)
      • 20. Hata, A.Y., Wolf, D.F.: ‘Feature detection for vehicle localization in urban environments using a multilayer LIDAR’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (2), pp. 420429.
    21. 21)
      • 21. Lee, S., Lee, B.-H., Lee, J.-E., et al: ‘Statistical characteristic-based road structure recognition in automotive FMCW radar systems’, IEEE Trans. Intell. Transp. Syst., 2018, pp. 112, doi: 10.1109/TITS.2018.2865588.
    22. 22)
      • 22. Lee, S., Lee, B.-H., Lee, J.-E., et al: ‘Iron tunnel recognition using statistical characteristics of received signals in automotive radar systems’. IEEE Int. Radar. Symp., Bonn, Germany, June 2018.
    23. 23)
      • 23. Minkler, G., Minkler, J.: ‘CFAR: the principles of automatic radar detection in clutter’ (Magellan, Baltimore, 1990).
    24. 24)
      • 24. Kong, L., Wang, B., Cui, G., et al: ‘Performance prediction of OS-CFAR for generalized Swerling-Chi fluctuating targets’, IEEE Trans. Aerosp. Electron. Syst., 2016, 52, (1), pp. 492500.
    25. 25)
      • 25. LeCun, Y., Boser, B., Denker, J.S., et al: ‘Handwritten digit recognition with a back-propagation network’, Proc. Adv. Neural Inf. Process. Syst., 1990, 2, pp. 396404.
    26. 26)
      • 26. Krizhevsky, A., Sutskever, I., Hinton, G., et al: ‘Imagenet classification with deep convolutional neural networks’. Proc. Adv. Neural Inf. Process. Syst., Stateline, NV, USA, 2012, pp. 11061114.
    27. 27)
      • 27. Graves, A., Mohamed, A.-R., Hinton, G.: ‘Speech recognition with deep recurrent neural networks’. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Vancouver, Canada, May 2013, pp. 66456649.
    28. 28)
      • 28. He, K., Zhang, X., Ren, S., et al: ‘Convolutional neural networks at constrained time cost’, IEEE Conf. on Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp. 37913799.
    29. 29)
      • 29. Chauvin, Y., Rumelhart, D.E.: ‘Back propagation: theory, architectures, and applications’ (Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1995), pp. 433486.
    30. 30)
      • 30. Cheriet, M.: ‘Character recognition systems’ (A John Wiley and Sons, New York, NY, USA, 2007, 1st edn.).
    31. 31)
      • 31. Al-Allaf, O.N.A, Abdalkader, S.A., Tamimi, A.A.: ‘Pattern recognition neural network for improving the performance of iris recognition system’, J. Sci. Eng. Res., 2013, 4, (6), pp. 661667.
    32. 32)
      • 32. Ahmed, M., Imtiaz, M.T., Khan, R.: ‘Movie recommendation system using clustering and pattern recognition network’. IEEE Computing and Communication Workshop and Conf. (CCWC), Las Vegas, USA, January 2018, pp. 143147.
    33. 33)
      • 33. Rumelhart, D.E., McClelland, J.L., David, E., et al: ‘Parallel distributed processing: explorations in the microstructures of cognition’ (MIT Press, Cambridge, MA, 1986, 1st edn.).
    34. 34)
      • 34. Karlik, B., Olgac, A.V.: ‘Performance analysis of various activation functions in generalized MLP architectures of neural networks’, Int. J. Artif. Intell. Expert Syst., 2011, 1, pp. 111122.
    35. 35)
      • 35. Wenrui, H., Simon, F.: ‘Neural network modeling of salinity variation in Apalachicola river’, Elsevier Signal Process. J., 2002, 36, (1), pp. 356362.

Related content

This is a required field
Please enter a valid email address