Abstract
Achieving fully autonomous driving cars is a considerable technological milestone that will have significant impact on many lives and the adaption of new technologies. The question of when this milestone will be achieved is currently being debated and contradictory forecasts are increasingly being made. In this chapter, the most important components of self-driving cars are presented and different approaches are discussed. We show what makes autonomous driving so challenging and what misjudgments have been made in the past. In particular, the role of artificial intelligence will be illuminated to give a clear picture of what progress is realistic in the coming years. Next, we discuss related challenges that need to be solved in the coming years. Based on our own research, we will also show how hard it is to interpret models, like neural networks, i.e., understanding why they make the decisions they make in the context of self-driving.
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References
Blum, A., & Rivest, R. L. (1989). Training a 3-node neural network is NP-complete. In Advances in neural information processing systems (pp. 494–501). http://papers.nips.cc/paper/125-training-a-3-node-neural-network-is-np-complete.pdf
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., et al. (2016). End to end learning for self-driving cars. arXiv:1604.07316.
Bojarski, M., Yeres, P., Choromanska, A., Choromanski, K., Firner, B., Jackel, L., et al. (2017). Explaining how a deep neural network trained with end-to-end learning steers a car. arXiv:1704.07911.
Borowiec, S. (2016). Alphago seals 4-1 victory over go grandmaster lee sedol. Retrieved August 1, 2018, from http://www.theguardian.com/technology/2016/mar/15/googles-alphago-seals-4-1-victory-over-grandmaster-lee-sedol.
Connected Automated Driving Europe. (2019). How do automakers perform with their self-driving car timeline? Retrieved December 15, 2019, from http://connectedautomateddriving.eu/mediaroom/how-do-automakers-perform-with-their-self-driving-car-timeline/
Cracknell, A. P. (2007). Introduction to remote sensing. Boca Raton: CRC Press.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248–255). Piscataway: IEEE.
Erhan, D., Bengio, Y., Courville, A., & Vincent, P. (2009). Visualizing higher-layer features of a deep network. University of Montreal, 1341(3), 1.
Fridman, L. (2019a). Elon musk: Neuralink, AI, autopilot, and the pale blue dot. Retrieved December 15, 2019, from http://lexfridman.com/elon-musk-2/
Fridman, L. (2019b). Tesla vehicle deliveries and autopilot mileage statistics. Retrieved December 15, 2019, from http://lexfridman.com/tesla-autopilot-miles-and-vehicles/
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.
Ioannidis, J. P. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097–1105). http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.
Lin, H. W., Tegmark, M., & Rolnick, D. (2017). Why does deep and cheap learning work so well? Journal of Statistical Physics, 168(6), 1223–1247.
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. https://web.archive.org/web/20070826230310/; http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html
Minsky, M., & Papert, S. A. (1969). Perceptrons: An introduction to computational geometry. Cambridge: MIT Press.
Mund, S., Frank, R., Varisteas, G., & State, R. (2018). Visualizing the learning progress of self-driving cars. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 2358–2363). Piscataway: IEEE.
Nuseibeh, B., & Easterbrook, S. (2000). Requirements engineering: A roadmap. In Proceedings of the Conference on the Future of Software Engineering, pp. 35–46. New York: ACM.
NVIDIA Corporation. (2019). NVIDIA DRIVE: Scalable AI platform for autonomous driving. Retrieved December 15, 2018, from http://www.nvidia.com/en-us/self-driving-cars/drive-platform/
Ohnsman, A. (2018). Waymo racks up 10 million test miles ahead of launching its robotaxi business. Retrieved December 15, 2018, from http://www.forbes.com/sites/alanohnsman/2018/10/10/waymo-racks-up-10-million-test-miles-ahead-of-launching-its-robotaxi-business/#46de3097eb1f
Pomerleau, D. A. (1989). ALVINN: An autonomous land vehicle in a neural network. In Advances in neural information processing systems (pp. 305–313). http://papers.nips.cc/paper/95-alvinn-an-autonomous-land-vehicle-in-a-neural-network.pdf
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985). Learning internal representations by error propagation. Technical report, California University San Diego La Jolla Institute for Cognitive Science.
Russell, S. J., & Norvig, P. (2009). Artificial intelligence: A modern approach (3rd edn.). Upper Saddle River: Prentice Hall.
Samek, W., Wiegand, T., & Müller, K.-R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv:1708.08296.
Shanahan, M. (2015). The technological singularity. Cambridge: MIT Press.
Siddiqui, F. (2019). Tesla floats fully self-driving cars as soon as this year. Many are worried about what that will unleash. Retrieved December 15, 2019, from http://www.washingtonpost.com/technology/2019/07/17/tesla-floats-fully-self-driving-cars-soon-this-year-many-are-worried-about-what-that-will-unleash/
Society of Automotive Engineers. (2014). Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. Warrendale: Society of Automotive Engineers. https://www.sae.org/standards/content/j3016_201401/
Templeton, B. (2019). Elon Musk’s war on LIDAR: Who is right and why do they think that? Retrieved December 15, 2019, from http://www.forbes.com/sites/bradtempleton/2019/05/06/elon-musks-war-on-lidar-who-is-right-and-why-do-they-think-that/#305b7af32a3b
Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., et al. (2006). Stanley: The robot that won the DARPA grand challenge. Journal of field Robotics, 23(9), 661–692.
World Health Organization. (2019). Global status report on alcohol and health 2018. World Health Organization.
Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In European Conference on Computer Vision (pp. 818–833). Berlin: Springer.
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Mund, S., Glauner, P. (2020). Autonomous Driving on the Thin Trail of Great Opportunities and Dangerous Trust. In: Glauner, P., Plugmann, P. (eds) Innovative Technologies for Market Leadership. Future of Business and Finance. Springer, Cham. https://doi.org/10.1007/978-3-030-41309-5_12
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