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AI & SOCIETY

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27-06-2022 | Open Forum

Two-stage approach to solve ethical morality problem in self-driving cars

Authors: Akshat Chandak, Shailendra Aote, Aradhita Menghal, Urvi Negi, Shreyas Nemani, Shubham Jha

Published in: AI & SOCIETY | Issue 2/2024

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Abstract

Ethical morality is one of the significant issues in self-driving cars. The paper provides a newer approach to solve the ethical decision problems in self-driving cars until there is no concrete ethical decision to all problems. This paper gives a two-way approach to solve a problem, with first being the mapping of problem to the solution already known or which has a fixed set of solutions and action priorities defined to a problem previously. Now, if no solution is found or mapping is unsuccessful, then the second stage activates, where the solution from Deep Q-learning model is calculated. It estimates the best Q value and returns that solution or action which maximizes the reward at that instance. The reward function is designed with decreasing priorities and acts accordingly, where the users can change or define their priorities if needed. The case study and results show that the solution that is present in the paper will lead to solving ethical morality problems in self-driving cars up to a great extent.

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Awad E, Dsouza S, Kim R, Schulz J, Henrich J, Shariff A, Bonnefon J-F and Rahwan I (2018) The moral machine experiment. Nature 563(7729):59–64. https://doi.org/10.1038/s41586-018-0637-6. [Source : https://www.moralmachine.net/]

Bonnefon J-F, Shariff A, Rahwan I (2016) The Social dilemma of autonomous vehicles. Science. https://doi.org/10.1126/science.aaf2654CrossRef

Bougie N, Ichise R (2021) Fast and slow curiosity for high-level exploration in reinforcement learning. Appl Intell 51:1086–1107. https://doi.org/10.1007/s10489-020-01849-3CrossRef

Diuk C, Cohen A and Littman ML (2008) An object-oriented representation for efficient reinforcement learning. In: Proceedings of the 25th international conference on Machine learning, pp 240–247. ACM

Dosovitskiy A, Ros G, Codevilla F, Lopez A and Koltun V (2017) CARLA: an open urban driving simulator. In: Conference on robot learning (pp 1–16). PMLR

Ethically aligned design (2018) A vision for prioritizing human well-being with autonomous and intelligent systems. http://standards.ieee.org/develop/indconn/ec/autonomous_systems.html

Ethik-Kommission (2017) Automatisiertes und vernetztes fahren. Tech Rep. http://www.bmvi.de/berichtethikkommission

European Parliament (2016) Ethical aspects of cyber-physical systems. Scientific Foresight Study. https://goo.gl/Fp5Wjs

Fridman L, Terwilliger J and Jenik B (2018) Deeptraffic: Crowdsourced hyperparameter tuning of deep reinforcement learning systems for multi-agent dense traffic navigation. arXiv preprint. arXiv:1801.02805

Holstein T, Dodig-Crnkovic G (2018) Avoiding the intrinsic unfairness of the trolley problem. IEEE/ACM Int Workshop Softw Fairness (FairWare) 2018:32–37. https://doi.org/10.23919/FAIRWARE.2018.8452918CrossRef

Holstein T, Dodig-Crnkovic G, Pelliccione P (2018) Ethical and social aspects of self-driving cars. ARXIV’18, January 2018, Gothenburg, Sweden

Kaelbling LP, Littman ML and Moore AW (1996) Reinforcement learning: a survey. CoRR, cs.AI/9605103

Karnouskos S (2020) Self-driving car acceptance and the role of ethics. IEEE Trans Eng Manage 67(2):252–265. https://doi.org/10.1109/TEM.2018.2877307CrossRef

Kiran BR, Sobh I, Talpaert V, Mannion P, Sallab AAA, Yogamani SK and Pérez P (2020) Deep reinforcement learning for autonomous driving: a survey. CoRR, abs/2002.00444. https://arxiv.org/abs/2002.00444

Lillicrap TP, Hunt JJ, Pritzel A, Heess N, Erez T, Tassa Y, Silver D and Wierstra D (2015) Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971

Maas AL, Hannun AY, Ng AY (2013) Rectifier Nonlinearities improve neural network acoustic models. ICML

Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D and Riedmiller M (2013) Playing atari with deep reinforcement learning. arXiv preprint. arXiv:1312.5602

Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, GOstrovski et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533CrossRef

NHTSA (2015) Critical reasons for crashes investigated in the national motor vehicle crash causation survey. Tech Rep. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812115

Riedmiller M, Gabel T, Hafner R, Lange S (2009) Reinforcement learning for robot soccer. Auton Robot 27(1):55–73CrossRef

Schaul T, Quan J, Antonoglou I and Silver D (2015) Prioritized experience replay. arXiv preprint arXiv:1511.05952

Shalev-Shwartz S, Shammah S and Shashua A (2016) Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint. arXiv:1610.03295

Siam M, Elkerdawy S, Jagersand M and Yogamani S (2017) Deep semantic segmentation for automated driving: taxonomy, roadmap and challenges. In: 2017 IEEE 20th international conference on intelligent transportation systems (ITSC) (pp 1–8). IEEE

Sutton RS (1992) Introduction: the challenge of reinforcement learning. In: Sutton RS (ed) Reinforcement learning. The springer international series in engineering and computer science (knowledge representation, learning and expert systems), vol 173. Springer, Boston. https://doi.org/10.1007/978-1-4615-3618-5_1CrossRef

Sutton RS, Barto AG (2015) Reinforcement learning: an introduction. A Bradford book. The MIT Press Cambridge, Cambridge

Taylor ME and Stone P (2009) Transfer learning for reinforcement learning domains: a survey. J Mach Learn Res 10(56):1633–1685. http://jmlr.org/papers/v10/taylor09a.html

Tesauro G (1995) Temporal difference learning and td-gammon. Commun ACM 38(3):58–68CrossRef

Todorov E, Erez T and Tassa Y (2012) Mujoco: a physics engine for model-based control. In: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, pages 5026–5033. IEEE

Urmson C, Whittaker WR (2008) Self-driving cars and the urban challenge. IEEE Intell Syst 23(2):66–68CrossRef

Valdes-Dapena P (2016) Volvo promises death-proof cars by 2020. http://money.cnn.com/2016/01/20/luxury/volvo-no-death-crash-cars-2020

van Hasselt H, Guez A and Silver D (2016a) Deep reinforcement learning with double Q-learning. In: Proceedings of the AAAI Conference on Artificial Intelligence 30(1). Available at: https://ojs.aaai.org/index.php/AAAI/article/view/10295 (Accessed: 18 Mar 2022)

Van Hasselt H, Guez A and Silver D (2016b) Deep reinforcement learning with double q-learning. In AAAI, pp 2094–2100

Wang, P. and Chan, C.Y., 2017, October. Formulation of deep reinforcement learning architecture toward autonomous driving for on-ramp merge. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) (pp 1–6). IEEE

Watkins CJCH, Dayan P (1992) Q-learning. Mach Learn 8(3–4):279–292CrossRef

Zhang Y, Huang M and Hao D (2010) The four-level valuation model of ethical decision-making. In: 2010 2nd IEEE International Conference on Information and Financial Engineering, 20 to 10, pp 806–809. https://doi.org/10.1109/ICIFE.2010.5609478

Title: Two-stage approach to solve ethical morality problem in self-driving cars
Authors: Akshat Chandak
Shailendra Aote
Aradhita Menghal
Urvi Negi
Shreyas Nemani
Shubham Jha
Publication date: 27-06-2022
Publisher: Springer London
Published in: AI & SOCIETY / Issue 2/2024
Print ISSN: 0951-5666
Electronic ISSN: 1435-5655
DOI: https://doi.org/10.1007/s00146-022-01517-9

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