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%.

References

1. 1)
  - 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. 111–122.
2. 2)
  - 7. Schneider, M.: ‘Automotive radar – status and trends’. Proc. German Microwave Conf., Ulm, Germany, April 2005, pp. 351–354.
3. 3)
  - 8. Nagy, L.L.: ‘Electromagnetic reflectivity characteristics of road surfaces’, IEEE Trans. Veh. Technol., 1974, IT-23, (4), pp. 117–124.
4. 4)
  - 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. 631–642.
5. 5)
  - 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. 396–404.
6. 6)
  - 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. 492–500.
7. 7)
  - 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.
8. 8)
  - 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. 109–112.
9. 9)
  - 1. BIS Research: ‘Global automotive sensor market demand, supply and opportunities: estimation and forecast of (2015–2022)’ (BIS Research, Fremont, CA, USA, 2015), pp. 1–48.
10. 10)
  - 35. Wenrui, H., Simon, F.: ‘Neural network modeling of salinity variation in Apalachicola river’, Elsevier Signal Process. J., 2002, 36, (1), pp. 356–362.
11. 11)
  - 4. Eriksson, L., Broden, S.: ‘High performance automotive radar’, Microw. J., 1996, 49, pp. 24–238.
12. 12)
  - 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. 1–12, doi: 10.1109/TITS.2018.2865588.
13. 13)
  - 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. 2457–2466.
14. 14)
  - 30. Cheriet, M.: ‘Character recognition systems’ (A John Wiley and Sons, New York, NY, USA, 2007, 1st edn.).
15. 15)
  - 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.).
16. 16)
  - 6. Rohling, H., Meinecke, M.-M.: ‘Waveform design principles for automotive radar systems’. Proc. CIE Int. Conf. Radar, Beijing, China, 2001, pp. 1–4.
17. 17)
  - 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. 661–667.
18. 18)
  - 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. 4412–4418.
19. 19)
  - 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. 6645–6649.
20. 20)
  - 17. Broggi, A., Cerri, P., Medici, P., et al: ‘Real time road signs recognition’. IEEE Intelligent Vehicles Symp., Istanbul, Turkey, June 2007, pp. 981–986.
21. 21)
  - 2. Skolnik, M.I.: ‘Introduction to radar systems’ (McGraw-Hill, New York, NY, USA, 2001, 3rd edn.).
22. 22)
  - 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. 3791–3799.
23. 23)
  - 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. 143–147.
24. 24)
  - 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. 1–11.
25. 25)
  - 29. Chauvin, Y., Rumelhart, D.E.: ‘Back propagation: theory, architectures, and applications’ (Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1995), pp. 433–486.
26. 26)
  - 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. 578–583.
27. 27)
  - 3. Jones, W.D.: ‘Keeping cars from crashing’, IEEE Spectr., 2001, 38, (9), pp. 40–45.
28. 28)
  - 5. Rohling, H.: ‘A 77 GHz automotive radar system for AICC applications’. Proc. Int. Conf. Microwaves and Radar (MIKON98), Krakow, Poland, 1998.
29. 29)
  - 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. 1106–1114.
30. 30)
  - 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. 400–406.
31. 31)
  - 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. 420–429.
32. 32)
  - 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. 120–125.
33. 33)
  - 9. Schneider, R., Didascalou, D., Wiesbeck, W.: ‘Impact of road surfaces on millimeter-wave propagation’, IEEE Trans. Veh. Technol., 2000, 49, (4), pp. 1314–1320.
34. 34)
  - 11. Matsunami, I., Kajiwara, A.: ‘Clutter suppression scheme for vehicle radar’. IEEE Radio and Wireless Symp., LA, USA, January 2010, pp. 320–323.
35. 35)
  - 23. Minkler, G., Minkler, J.: ‘CFAR: the principles of automatic radar detection in clutter’ (Magellan, Baltimore, 1990).

Road structure classification through artificial neural network for automotive radar systems

References

Related content