Fetching the paper…
Reading the bibliography…
To perform high speed tasks, sensors of autonomous cars have to provide as much information in as few time steps as possible.
A. P. Dempster, “A generalization of bayesian inference,” Journal of the Royal Statistical Society: Series B (Methodological) , vol. 30, no. 2, pp. 205–232, 1968
1968
Earlier work this paper cites.
A. Elfes, “Using occupancy grids for mobile robot perception and navigation,” Computer , no. 6, pp. 46–57, 1989
1989
Earlier work this paper cites.
S. B. Thrun, “Exploration and model building in mobile robot domains,” in Neural Networks, 1993., IEEE International Conference on . IEEE, 1993, pp. 175–180
1993
Earlier work this paper cites.
D. A. Nix and A. S. Weigend, “Estimating the mean and variance of the target probability distribution,” in Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference On , vol. 1. IEEE, 1994, pp. 55–60
1994
Earlier work this paper cites.
J. W. Van Dam, B. J. Kröse, and F. C. Groen, “Neural network applications in sensor fusion for an autonomous mobile robot,” in Reasoning with uncertainty in Robotics . Springer, 1996, pp. 263–278
1996
Earlier work this paper cites.
D. Pagac, E. M. Nebot, and H. Durrant-Whyte, “An evidential approach to map-building for autonomous vehicles,” IEEE Transactions on Robotics and Automation , vol. 14, no. 4, pp. 623–629, 1998
1998
Earlier work this paper cites.
S. Thrun, W. Burgard, and D. Fox, Probabilistic robotics . MIT press, 2005
2005
Earlier work this paper cites.
F. Fleuret, J. Berclaz, R. Lengagne, and P. Fua, “Multicamera people tracking with a probabilistic occupancy map,” IEEE transactions on pattern analysis and machine intelligence , vol. 30, no. 2, pp. 267–282, 2008
2008
Earlier work this paper cites.
J. Moras, V. Cherfaoui, and P. Bonnifait, “Moving objects detection by conflict analysis in evidential grids,” in Intelligent Vehicles Symposium (IV), 2011 IEEE . IEEE, 2011, pp. 1122–1127
2011
Earlier work this paper cites.
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Cited alongside, same era.
H. Carrillo, P. Dames, V. Kumar, and J. A. Castellanos, “Autonomous robotic exploration using occupancy grid maps and graph slam based on shannon and rényi entropy,” in Robotics and Automation (ICRA), 2015 IEEE International Conference on . IEEE, 2015, pp. 487–494
2015
Cited alongside, same era.
K. Werber, M. Rapp, J. Klappstein, M. Hahn, J. Dickmann, K. Dietmayer, and C. Waldschmidt, “Automotive radar gridmap representations,” in Microwaves for Intelligent Mobility (ICMIM), 2015 IEEE MTT-S International Conference on . IEEE, 2015, pp. 1–4
2015
Cited alongside, same era.
K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” in Advances in Neural Information Processing Systems , 2015, pp. 3483–3491
J. Lombacher, K. Laudt, M. Hahn, J. Dickmann, and C. Wöhler, “Semantic radar grids,” in Intelligent Vehicles Symposium (IV), 2017 IEEE . IEEE, 2017, pp. 1170–1175
2017
Later among the works it cites.
2017
Later among the works it cites.
A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” in Advances in neural information processing systems , 2017, pp. 5574–5584
2017
Later among the works it cites.
A. Swief and M. El-Habrouk, “A survey of automotive driving assistance systems technologies,” in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) . IEEE, 2018, pp. 1–12
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Cited alongside, same era.
C. Loop, Q. Cai, S. Orts-Escolano, and P. A. Chou, “A closed-form bayesian fusion equation using occupancy probabilities,” in 3D Vision (3DV), 2016 Fourth International Conference on . IEEE, 2016, pp. 380–388
2016
Cited alongside, same era.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in international conference on machine learning , 2016, pp. 1050–1059
2016
Cited alongside, same era.
I. Osband, C. Blundell, A. Pritzel, and B. Van Roy, “Deep exploration via bootstrapped dqn,” in Advances in neural information processing systems , 2016, pp. 4026–4034
2016
Cited alongside, same era.
A. Jøsang, “Subjective logic: A formalism for reasoning under uncertainty, ser,” Artificial Intelligence: Foundations, Theory and Algorithms. Springer International Publishing Switzerland , 2016
2016
Cited alongside, same era.
M. Sensoy, L. Kaplan, and M. Kandemir, “Evidential deep learning to quantify classification uncertainty,” in Advances in Neural Information Processing Systems , 2018, pp. 3183–3193
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
J. Gast and S. Roth, “Lightweight probabilistic deep networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3369–3378
2018
Later among the works it cites.