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2021 | OriginalPaper | Buchkapitel

Neurocognitive–Inspired Approach for Visual Perception in Autonomous Driving

verfasst von : Alice Plebe, Mauro Da Lio

Erschienen in: Smart Cities, Green Technologies and Intelligent Transport Systems

Verlag: Springer International Publishing

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Abstract

Since the last decades, deep neural models have been pushing forward the frontiers of artificial intelligence. Applications that in the recent past were considered no more than utopian dreams, now appear to be feasible. The best example is autonomous driving. Despite the growing research aimed at implementing autonomous driving, no artificial intelligence can claim to have reached or closely approached the driving performance of humans, yet. While the early forms of artificial neural networks were aimed at simulating and understanding human cognition, contemporary deep neural networks are totally indifferent to cognitive studies, they are designed with pure engineering goals in mind. Several scholars, we included, argue that it urges to reconnect artificial modeling with an updated knowledge of how complex tasks are realized by the human mind and brain. In this paper, we will first try to distill concepts within neuroscience and cognitive science relevant for the driving behavior. Then, we will identify possible algorithmic counterparts of such concepts, and finally build an artificial neural model exploiting these components for the visual perception task of an autonomous vehicle. More specifically, we will point to four neurocognitive theories: the simulation theory of cognition; the Convergence–divergence Zones hypothesis; the transformational abstraction hypothesis; the free–energy predictive theory. Our proposed model tries to combine a number of existing algorithms that most closely resonate with the assumptions of these four neurocognitive theories.

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Vorheriges Kapitel Power Grid Capacity Extension with Conditional Loads Instead of Physical Expansions

Nächstes Kapitel Dynamic Vehicle Routing Under Uncertain Energy Consumption and Energy Gain Opportunities

Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1–127 (2009)MATH

Bojarski, M., et al.: Explaining how a deep neural network trained with end-to-end learning steers a car. CoRR abs/1704.07911 (2017)

Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Jozefowicz, R., Bengio, S.: Generating sentences from a continuous space. CoRR abs/1511.06349 (2015)

Bracewell, R.: Fourier Analysis and Imaging. Springer, Heidelberg (2003). https://doi.org/10.1007/978-1-4419-8963-5CrossRefMATH

Buckner, C.: Empiricism without magic: transformational abstraction in deep convolutional neural networks. Synthese 195, 5339–5372 (2018)

Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. CoRR abs/1405.3531 (2014)

Chui, M., et al.: Notes from the AI frontier: insights from hundreds of use cases. Technical report, April, McKinsey Global Institute (2018)

Damasio, A.: Time-locked multiregional retroactivation: a systems-level proposal for the neural substrates of recall and recognition. Cognition 33, 25–62 (1989)

Eickenberg, M., Gramfort, A., Varoquaux, G., Thirion, B.: Seeing it all: convolutional network layers map the function of the human visual system. NeuroImage 152, 184–194 (2017)

10.

Felleman, D.J., Van Essen, D.C.: Distributed hierarchical processing in the primate cerebral cortex. Cerebr. Cortex 1, 1–47 (1991)

11.

Fodor, J.: Modularity of Mind: and Essay on Faculty Psychology. MIT Press, Cambridge (1983)

12.

Freedman, D.J., Riesenhuber, M., Poggio, T., Miller, E.K.: Categorical representation of visual stimuli in the primate prefrontal cortex. Science 291, 312–316 (2001)

13.

Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010)

14.

Friston, K., Fitzgerald, T., Rigoli, F., Schwartenbeck, P., Pezzulo, G.: Active inference: a process theory. Neural Comput. 29, 1–49 (2017)MathSciNetMATH

15.

Friston, K., Stephan, K.E.: Free-energy and the brain. Synthese 159, 417–458 (2007)

16.

Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 193–202 (1980)MATH

17.

Gilbert, C.D., Wiesel, T.N.: Morphology and intracortical projections of functionally characterised neurones in the cat visual cortex. Nature 280, 120–125 (1979)

18.

Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)

19.

Grillner, S., Wallén, P.: Innate versus learned movements - a false dichotomy. Progress Brain Res. 143, 1–12 (2004)

20.

Güçlü, U., van Gerven, M.A.J.: Unsupervised feature learning improves prediction of human brain activity in response to natural images. PLoS Comput. Biol. 10, 1–16 (2014)

21.

Güçlü, U., van Gerven, M.A.J.: Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci. 35, 10005–10014 (2015)

22.

Hassabis, D., Kumaran, D., Summerfield, C., Botvinick, M.: Neuroscience-inspired artificial intelligence. Neuron 95, 245–258 (2017)

23.

Hazelwood, K., et al.: Applied machine learning at Facebook: a datacenter infrastructure perspective. In: IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 620–629 (2018)

24.

Hesslow, G.: The current status of the simulation theory of cognition. Brain 1428, 71–79 (2012)

25.

Hinton, G., Zemel, R.S.: Autoencoders, minimum description length and Helmholtz free energy. In: Advances in Neural Information Processing Systems, pp. 3–10 (1994)

26.

Hinton, G.E., McClelland, J.L., Rumelhart, D.E.: Distributed representations. In: Rumelhart and McClelland [53], pp. 77–109

27.

Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 28, 504–507 (2006)MathSciNetMATH

28.

Hubel, D., Wiesel, T.: Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex. J. Physiol. 160, 106–154 (1962)

29.

Hubel, D., Wiesel, T.: Receptive fields and functional architecture of mokey striate cortex. J. Physiol. 195, 215–243 (1968)

30.

Jeannerod, M.: Neural simulation of action: a unifying mechanism for motor cognition. NeuroImage 14, S103–S109 (2001)

31.

Jones, W., Alasoo, K., Fishman, D., Parts, L.: Computational biology: deep learning. Emerg. Top. Life Sci. 1, 136–161 (2017)

32.

Khan, S., Tripp, B.P.: One model to learn them all. CoRR abs/1706.05137 (2017)

33.

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of International Conference on Learning Representations (2014)

34.

Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: Proceedings of International Conference on Learning Representations (2014)

35.

Kosslyn, S.M.: Image and Brain: the Resolution of the Imagery Debate. MIT Press, Cambridge (1994)

36.

Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1090–1098 (2012)

37.

Kulkarni, T.D., Whitney, W.F., Kohli, P., Tenenbaum, J.B.: Deep convolutional inverse graphics network. In: Advances in Neural Information Processing Systems, pp. 2539–2547 (2015)

38.

Larochelle, H., Bengio, Y., Louradour, J., Lamblin, P.: Exploring strategies for training deep neural networks. J. Mach. Learn. Res. 1, 1–40 (2009)MATH

39.

Li, J., Cheng, H., Guo, H., Qiu, S.: Survey on artificial intelligence for vehicles. Autom. Innov. 1, 2–14 (2018)

40.

Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing 234, 11–26 (2017)

41.

Marblestone, A.H., Wayne, G., Kording, K.P.: Toward an integration of deep learning and neuroscience. Front. Comput. Neurosci. 10 (2016). Article 94

42.

Meyer, K., Damasio, A.: Convergence and divergence in a neural architecture for recognition and memory. Trends Neurosci. 32, 376–382 (2009)

43.

Moulton, S.T., Kosslyn, S.M.: Imagining predictions: mental imagery as mental emulation. Philos. Trans. Roy. Soc. B 364, 1273–1280 (2009)

44.

Newen, A., Bruin, L.D., Gallagher, S. (eds.): The Oxford Handbook of 4E Cognition. Oxford University Press, Oxford (2018)

45.

Olier, J.S., Barakova, E., Regazzoni, C., Rauterberg, M.: Re-framing the characteristics of concepts and their relation to learning and cognition in artificial agents. Cogn. Syst. Res. 44, 50–68 (2017)

46.

Plebe, A., Da Lio, M., Bortoluzzi, D.: On reliable neural network sensorimotor control in autonomous vehicles. IEEE Trans. Intell. Transp. Syst. 21, 711–722 (2019)

47.

Plebe, A., Donà, R., Rosati Papini, G.P., Da Lio, M.: Mental imagery for intelligent vehicles. In: Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems, pp. 43–51. INSTICC, SciTePress (2019)

48.

Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: Xing, E.P., Jebara, T. (eds.) Proceedings of Machine Learning Research, pp. 1278–1286 (2014)

49.

Rolls, E., Deco, G.: Computational Neuroscience of Vision. Oxford University Press, Oxford (2002)MATH

50.

Ros, G., Vazquez, L.S.J.M.D., Lopez, A.M.: The SYNTHIA dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 3234–3243 (2016)

51.

Rosenfeld, A., Kak, A.C.: Digital Picture Processing, 2nd edn. Academic Press, New York (1982)MATH

52.

Rumelhart, D.E., Durbin, R., Golden, R., Chauvin, Y.: Backpropagation: The basic theory. In: Chauvin, Y., Rumelhart, D.E. (eds.) Backpropagation: Theory, Architectures and Applications, pp. 1–34. Lawrence Erlbaum Associates, Mahwah (1995)

53.

Rumelhart, D.E., McClelland, J.L. (eds.): Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press, Cambridge (1986)

54.

Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

55.

Schwarting, W., Alonso-Mora, J., Rus, D.: Planning and decision-making for autonomous vehicles. Ann. Rev. Control Robot. Auton. Syst. 1, 8.1–8.24 (2018)

56.

Seger, C.A., Miller, E.K.: Category learning in the brain. Ann. Rev. Neurosci. 33, 203–219 (2010)

57.

Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Cardoso, M.J.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, J., et al. (eds.) Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 240–248 (2017)

58.

Tripp, B.P.: Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks. In: International Joint Conference on Neural Networks, pp. 3551–3560 (2017)

59.

Tschannen, M., Lucic, M., Bachem, O.: Recent advances in autoencoder-based representation learning. In: NIPS Workshop on Bayesian Deep Learning (2018)

60.

Ullman, S.: Using neuroscience to develop artificial intelligence. Science 363, 692–693 (2019)

61.

Van Essen, D.C.: Organization of visual areas in macaque and human cerebral cortex. In: Chalupa, L., Werner, J. (eds.) The Visual Neurosciences. MIT Press, Cambridge (2003)

62.

Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)MathSciNetMATH

63.

Šmídl, V., Quinn, A.: The Variational Bayes Method in Signal Processing. Springer, Heidelberg (2005). https://doi.org/10.1007/3-540-28820-1CrossRefMATH

64.

Wolpert, D.M., Diedrichsen, J., Flanagan, R.: Principles of sensorimotor learning. Nat. Rev. Neurosci. 12, 739–751 (2011)

65.

Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53CrossRef

66.

Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: Deconvolutional networks. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 7–15 (2010)

67.

Zeiler, M.D., Taylor, G.W., Fergus, R.: Adaptive deconvolutional networks for mid and high level feature learning. In: International Conference on Computer Vision, pp. 6–14 (2011)

68.

Zhao, J., Mathieu, M., Goroshin, R., LeCun, Y.: Stacked what-where auto-encoders. In: International Conference on Learning Representations, pp. 1–12 (2016)

Titel: Neurocognitive–Inspired Approach for Visual Perception in Autonomous Driving
verfasst von: Alice Plebe
Mauro Da Lio
Verlag: Springer International Publishing
Buch: Smart Cities, Green Technologies and Intelligent Transport Systems
Print ISBN: 978-3-030-68027-5

Electronic ISBN: 978-3-030-68028-2

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-68028-2_6

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