Abstract
In autonomous driving, predicting the potential collision on road from the first-person view is a very challenging task. Previous study developed a computational model for this task, called LGMD2, which is inspired by a perfect biological visual system, Lobula Giant Movement Detectors, in locusts. LGMD enables locusts to swarm and be free of collision. However, LGMD2 model assumes the moving objects are darker than the background, which rarely happens in real-world scenarios. Meanwhile, its computation structure (ON & OFF pathways) has mutual interference and produces incomplete information. Thus, LGMD2 results in a low prediction accuracy. In this study, we amend the assumption that is more in line with the reality, and propose a novel single-pathway LGMD2 model, SLGMD2. It avoids the interference and generates more complete signal for prediction. To evaluate the effectiveness of SLGMD2, we collect a new first-person view vehicle collision dataset. The experiment on both two public datasets and the newly collected dataset shows state-of-the-art performance of our proposed SLGMD2 model.
This work was supported in part by the Guangdong Provincial Key Research and Development Programme under Grant 2021B0101410002.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bao, W., Yu, Q., Kong, Y.: Uncertainty-based traffic accident anticipation with spatio-temporal relational learning. In: Proceedings of the 28th ACM International Conference on Multimedia (2020)
Borst, A., Euler, T.: Seeing things in motion: models, circuits, and mechanisms. Neuron 71(6), 974–994 (2011)
Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621–11631 (2020)
Chan, F.-H., Chen, Y.-T., Xiang, Yu., Sun, M.: Anticipating accidents in dashcam videos. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10114, pp. 136–153. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54190-7_9
Chang, M.F., et al.: Argoverse: 3D tracking and forecasting with rich maps. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8748–8757 (2019)
Fu, Q., Hu, C., Peng, J., Rind, F.C., Yue, S.: A robust collision perception visual neural network with specific selectivity to darker objects. IEEE Trans. Tybernet. 50(12), 5074–5088 (2019)
Fu, Q., Hu, C., Peng, J., Yue, S.: Shaping the collision selectivity in a looming sensitive neuron model with parallel on and off pathways and spike frequency adaptation. Neural Netw. 106, 127–143 (2018)
Gabbiani, F., Krapp, H.G., Koch, C., Laurent, G.: Multiplicative computation in a visual neuron sensitive to looming. Nature 420(6913), 320–324 (2002)
Hatsopoulos, N., Gabbiani, F., Laurent, G.: Elementary computation of object approach by a wide-field visual neuron. Science 270(5238), 1000–1003 (1995)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
He, L., Aouf, N., Whidborne, J.F., Song, B.: Integrated moment-based LGMD and deep reinforcement learning for UAV obstacle avoidance. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 7491–7497. IEEE (2020)
Herzig, R., et al.: Spatio-temporal action graph networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 2347–2356 (2019)
Huang, X., Wang, P., Cheng, X., Zhou, D., Geng, Q., Yang, R.: The ApolloScape open dataset for autonomous driving and its application. IEEE Trans. Pattern Anal. Mach. Intell. 42(10), 2702–2719 (2019)
Jayachandran, D., et al.: A low-power biomimetic collision detector based on an in-memory molybdenum disulfide photodetector. Nature Electron. 3(10), 646–655 (2020)
Kennedy, J.S.: The migration of the desert locust (schistocerca gregaria forsk.). i. the behaviour of swarms. ii. a theory of long-range migrations. In: Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, pp. 163–290 (1951)
Liu, W., Luo, W., Lian, D., Gao, S.: Future frame prediction for anomaly detection-a new baseline. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6536–6545 (2018)
Qinbing, Fu, S.Y., Hu, C.: Bio-inspired collision detector with enhanced selectivity for ground robotic vision system. In: Richard, C., Wilson, E.R.H., Smith, W.A.P. (eds.) Proceedings of the British Machine Vision Conference (BMVC), pp. 6.1–6.13. BMVA Press (2016). https://doi.org/10.5244/C.30.6
Rind, F.C., Bramwell, D.: Neural network based on the input organization of an identified neuron signaling impending collision. J. Neurophysiol. 75(3), 967–985 (1996)
Rind, F.C., et al.: Two identified looming detectors in the locust: ubiquitous lateral connections among their inputs contribute to selective responses to looming objects. Sci. Rep. 6(1), 1–16 (2016)
Simmons, P.J., Rind, F.C.: Responses to object approach by a wide field visual neurone, the LGMD2 of the locust: characterization and image cues. J. Comp. Physiol. A 180(3), 203–214 (1997)
Suzuki, T., Kataoka, H., Aoki, Y., Satoh, Y.: Anticipating traffic accidents with adaptive loss and large-scale incident DB. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3521–3529 (2018)
Yao, Y., Wang, X., Xu, M., Pu, Z., Atkins, E., Crandall, D.: When, where, and what? A new dataset for anomaly detection in driving videos. arXiv preprint arXiv:2004.03044 (2020)
Yue, S., Rind, F.C.: A collision detection system for a mobile robot inspired by the locust visual system. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 3832–3837. IEEE (2005)
Yue, S., Rind, F.C., Keil, M.S., Cuadri, J., Stafford, R.: A bio-inspired visual collision detection mechanism for cars: optimisation of a model of a locust neuron to a novel environment. Neurocomputing 69(13–15), 1591–1598 (2006)
Zeng, K.H., Chou, S.H., Chan, F.H., Carlos Niebles, J., Sun, M.: Agent-centric risk assessment: accident anticipation and risky region localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2222–2230 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, S., Lei, G., Liang, X. (2022). A Single-Pathway Biomimetic Model for Potential Collision Prediction. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13536. Springer, Cham. https://doi.org/10.1007/978-3-031-18913-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-18913-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-18912-8
Online ISBN: 978-3-031-18913-5
eBook Packages: Computer ScienceComputer Science (R0)