Abstract
Research into self-driving cars has grown enormously in the last decade primarily due to the advances in the fields of machine intelligence and image processing. An under-appreciated aspect of self-driving cars is actively avoiding high traffic zones, low visibility zones, and routes with rough weather conditions by learning different conditions and making decisions based on trained experiences. This paper addresses this challenge by introducing a novel hierarchical structure for dynamic path planning and experiential learning for vehicles. A multistage system is proposed for detecting and compensating for weather, lighting, and traffic conditions as well as a novel adaptive path planning algorithm named Checked State A3C. This algorithm improves upon the existing A3C Reinforcement Learning (RL) algorithm by adding state memory which provides the ability to learn an adaptive model of the best decisions to take from experience.
S. Ganapathi Subramanian, J. S. Sambee and B. Ghojogh contributed equally to this work.
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References
Barnum, P.C., Narasimhan, S., Kanade, T.: Analysis of rain and snow in frequency space. Int. J. Comput. Vis. 86(2), 256–274 (2010)
SAE On-Road Automated Vehicle Standards Committee, et al.: Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. SAE Standard J3016, pp. 1–16 (2014)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., Murphy, K.: Speed/accuracy trade-offs for modern convolutional object detectors. Honolulu, Hawaii (2016)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Maddern, W., Pascoe, G., Linegar, C., Newman, P.: 1 year, 1000km: the Oxford RobotCar dataset. Int. J. Robot. Res. (IJRR) 36(1), 3–15 (2017)
Milford, M.J., Wyeth, G.F.: SeqSLAM: visual route-based navigation for sunny summer days and stormy winter nights. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 1643–1649. IEEE (2012)
Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016)
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)
Tausworthe, R.C.: Random numbers generated by linear recurrence modulo two. Math. Comput. 19(90), 201–209 (1965)
Tzutalin, D.: LabelImg annotation tool. https://github.com/tzutalin/labelImg. Accessed 18 Oct 2017
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Ganapathi Subramanian, S., Sambee, J.S., Ghojogh, B., Crowley, M. (2018). Decision Assist for Self-driving Cars. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_44
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