Design of an Efficient VLSI Architecture for Collision Detection Based on Insect's Visual Interneuron

곤충의 시각 신경망 기반 충돌감지 기술의 효율적인 VLSI 구조 설계

  • Jeong, Sooyong ;
  • Lee, Jaehyeon ;
  • Song, Deokyong ;
  • Park, Taegeun
  • 정수용 ;
  • 이재현 ;
  • 송덕용 ;
  • 박태근
  • Received : 2018.08.24
  • Accepted : 2018.10.30
  • Published : 2018.12.01


In this research, the collision detection system based on insect's visual interneuron has been designed. The lobula giant movement detector (LGMD) corresponds to the movement value that increases in direct collision process. If the collision is detected by the LGMD only, it could generate a crash warning even in a non-collision situation, resulting in a lot of false alarms. Directionally sensitive movement detectors (DSMD) are directionally sensitive algorithm based on the elementary movement detectors (EMD) in four directions (up, down, left, and right). In this paper, we propose an efficient VLSI architecture for a realtime collision detection system that is robust to the surrounding environment while improving accuracy. The proposed architecture is synthesized with Dongbu Hightech 110nm standard cell library and shows 333MHz of maximum operating frequency and requires 8400 gates with about 16.5KB of internal memories.


Intelligent vehicle;Collision detection;VLSI architecture;DSMD;LGMD


  1. T. Gandhi and M. M. Trivedi, "Pedestrian protection systems: Issues, survey, and challenges", IEEE Trans. on Intell. Trans. Syst., vol. 8, no. 3, pp. 413-430, 2007.
  2. K. C. Fuerstenberg, "Pedestrian protection using laserscanners", IEEE Intell. Trans. Syst. Conf., pp. 437-442, 2005.
  3. P. Kumar, S. Ranganath, H. Weimin, and K. Sengupta, "Framework for real-time behavior interpretation from traffic video", IEEE Trans. Intell. Trans. Syst., vol. 6, no. 1, pp. 43-53, 2005.
  4. Y. Abramson and B. Steux, "Hardware-friendly pedestrian detection and impact prediction", IEEE Intell. Veh. Symp. Conf., pp. 590-595, 2004.
  5. M. Strickland1, G. Fainekos1, and H. Amor, "Deep predictive models for collision risk assessment in autonomous driving", IEEE Int. Conf. on Robotics and Automation, 2018.
  6. S. H. Park, B. Kim, C. M. Kang, C. C. Chung, and J. W. Choi, "Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder Architecture", IEEE Intell. Vehicles Symposium, 2018.
  7. F. Rind and P. Simmons, "Seeing what is coming: building collision sensitive ons", Trends Neurosci. vol. 22 pp. 215-220, 1999.
  8. R. Stafford, R. D. Santer, F. C. Rind, "A bio-inspired visual collision detection mechanism for cars: Combining insect inspired neurons to create a robust system", BioSystems, vol. 87, pp. 164-171, 2006.
  9. J. Cuadri, G. Linana, R. Stafford, M. Keila, and E. Roca, "A bioinspired collision detection algorithm for VLSI implementation", Bioengineered and Bioinspired Systems II, vol. 5839, 2005.
  10. H. Liang, T. Morie, Y. Suzuki, K. Nakada, T. Miki, and H. Hayashi, "An FPGA-based collision warning system using hybrid approach", IEEE Int. Conf. on Hybrid Intell. Systems, pp. 30-35, 2007.
  11. G. Linan-Cembrano, L. Carranza, C. Rind, A. Zarandy, M. Soininen, and A. Rodriguez-Vazquez, "Insect-vision inspired collision warning vision processor for automobiles", IEEE Circuits and Systems Magazine, vol. 8 no. 2, pp. 6-24, 2008.


Supported by : 가톨릭대학교