531
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Autonomous Behavior Selection For Self-driving Cars Using Probabilistic Logic Factored Markov Decision Processes

, , , , , & show all
Article: 2304942 | Received 10 Oct 2023, Accepted 02 Jan 2024, Published online: 11 Mar 2024

References

  • Aksjonov, A., and V. Kyrki. 2022. A safety-critical decision-making and control framework combining machine-learning-based and rule-based algorithms. SAE International Journal of Vehicle Dynamics, Stability, and NVH 7 (3). arXiv preprint arXiv:2201.12819. doi:10.4271/10-07-03-0018.
  • Al-Nuaimi, M., S. Wibowo, H. Qu, J. Aitken, and S. Veres. 2021, SEP. Hybrid verification technique for decision-making of self-driving vehicles. Journal of Sensor & Actuator Networks 10(3):42. doi:10.3390/jsan10030042.
  • Avilés-Arriaga, H. H., L. E. Sucar, E. F. Morales, B. A. Vargas, J. Sánchez, and E. Corona. 2009. Markovito: a flexible and general service robot. Design and Control of Intelligent Robotic Systems 177:401–25.
  • Avilés, H., M. Negrete, R. Machucho, K. Rivera, D. Trejo, and H. Vargas. 2022. Probabilistic logic Markov decision processes for modeling driving behaviors in self-driving cars. Ibero-American Conference on Artificial Intelligence (IBERAMIA), 366–77, Cartagena de Indias, Colombia: Springer. Springer.
  • Boutilier, C., R. Dearden, and M. Goldszmidt. 2000 August. Stochastic dynamic programming with factored representations. Artificial Intelligence 121(1–2):49–107. doi:10.1016/S0004-3702(00)00033-3.
  • Bueno, T. P., D. D. Mauá, L. N. De Barros, and F. G. Cozman. 2016. Markov decision processes specified by probabilistic logic programming: representation and solution. 2016 5th Brazilian Conference on Intelligent Systems (BRACIS), 337–42, Recife, Brazil: IEEE.
  • Cai, Z., Q. Fan, R. S. Feris, and N. Vasconcelos. 2016. A unified multi-scale deep convolutional neural network for fast object detection. Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, 354–70, Springer.
  • Chavira, M., and A. Darwiche. 2008. On probabilistic inference by weighted model counting. Artificial Intelligence 172 (6–7):772–99. doi:10.1016/j.artint.2007.11.002.
  • Clark, K. L. 1978. Negation as failure. In Logic and data bases, ed., H. Gallaire and J. Minker. Boston, MA: Springer. doi: 10.1007/978-1-4684-3384-5_11.
  • Da Lio, M., A. Cherubini, G. P. R. Papini, and A. Plebe. 2023. Complex self-driving behaviors emerging from affordance competition in layered control architectures. Cognitive Systems Research 79:4–14. doi:10.1016/j.cogsys.2022.12.007.
  • Dortmans, E., and T. Punter. 2022. Behavior trees for smart robots practical guidelines for robot software development. Journal of Robotics 2022 (7):7419–30. doi:10.1155/2022/3314084.
  • Duan, J., S. Eben Li, Y. Guan, Q. Sun, and B. Cheng. 2020. Hierarchical reinforcement learning for self-driving decision-making without reliance on labelled driving data. IET Intelligent Transport Systems 14 (5):297–305. doi:10.1049/iet-its.2019.0317.
  • Fierens, D., G. Van den Broeck, J. Renkens, D. Shterionov, B. Gutmann, I. Thon, G. Janssens, and L. De Raedt. 2015. Inference and learning in probabilistic logic programs using weighted boolean formulas. Theory and Practice of Logic Programming 15 (3):358–401. doi:10.1017/S1471068414000076.
  • Hoey, J., R. St-Aubin, A. Hu, and C. Boutilier. 1999. SPUDD: Stochastic planning using decision diagrams. Proceedings of International Conference on Uncertainty in Artificial Intelligence (UAI ‘99), Stockholm, Sweden.
  • Lindqvist, B., S. S. Mansouri, A.-A. Agha-Mohammadi, and G. Nikolakopoulos. 2020. Nonlinear mpc for collision avoidance and control of UAVs with dynamic obstacles. IEEE Robotics and Automation Letters 5 (4):6001–08. doi:10.1109/LRA.2020.3010730.
  • Liu, Q., X. Li, S. Yuan, and Z. Li. 2021. Decision-making technology for autonomous vehicles: Learning-based methods, applications and future outlook. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 30–37, Indianapolis, IN, USA. IEEE.
  • Mishra, P. 2022. Model explainability for rule-based expert systems. In Practical explainable AI using python, ed. P. Mishra, 315–26. Berkeley, CA: Apress. doi:10.1007/978-1-4842-7158-2_13.
  • Mu, Z., W. Fang, S. Zhu, T. Jin, W. Song, X. Xi, Q. Huang, J. Gu, and S. Yuan. 2022. A multi-modal behavior planning framework for guide robot. 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO), Jinghong, China.
  • Padalkar, P., H. Wang, and G. Gupta. 2023. Nesyfold: A system for generating logic-based explanations from convolutional neural networks. arXiv preprint arXiv:2301.12667.
  • Paden, B., M. Čáp, S. Z. Yong, D. Yershov, and E. Frazzoli. 2016. A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Transactions on Intelligent Vehicles 1 (1):33–55. doi:10.1109/TIV.2016.2578706.
  • Pepe, G., M. Laurenza, D. Antonelli, and A. Carcaterra. 2019. A new optimal control of obstacle avoidance for safer autonomous driving. 2019 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE), 1–6, Turin, Italy.
  • Precup, R.-E., S. Preitl, J. Tar, M. Tomescu, M. Takacs, P. Korondi, and P. Baranyi, 2008 9. Fuzzy control system performance enhancement by iterative learning control. IEEE Transactions on Industrial Electronics 55(9):3461–75. Accessed November 27, 2023. doi:10.1109/TIE.2008.925322.
  • Puterman, M. L. 1994. Markov decision processes: Discrete stochastic dynamic programming. Hoboken, NJ, US: John Wiley & Sons.
  • Reyes, A., L. E. Sucar, P. H. Ibargüengoytia, and E. F. Morales. 2020, May. Planning under uncertainty applications in power plants using factored Markov decision processes. Energies 13(9):2302. doi:10.3390/en13092302.
  • Rigatos, G., P. Siano, D. Selisteanu, and R. E. Precup. 2016, Nonlinear optimal control of oxygen and carbon dioxide levels in blood. Intelligent Industrial Systems 3 (2):61–75. Accessed November 27, 2023. doi:10.1007/s40903-016-0060-y.
  • Riguzzi, F. 2022. Foundations of probabilistic logic programming: Languages, semantics, inference and learning. Denmark: River Publishers.
  • Sato, T. 1995. A statistical learning method for logic programs with distribution semantics. Proceedings of the 12th international conference on logic programming (ICLP’95), 715–29, Citeseer.
  • Smith, G., R. Petrick, and V. Belle. 2021. Intent recognition in smart homes with problog. 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Kassel, Germany.
  • Spanogiannopoulos, S., Y. Zweiri, and L. Seneviratne. 2022. Sampling-based non-holonomic path generation for self-driving cars. Journal of Intelligent & Robotic Systems 104(1). doi:10.1007/s10846-021-01440-z.
  • Sun, Z., J. Zou, D. He, Z. Man, and J. Zheng. 2020. Collision-avoidance steering control for autonomous vehicles using neural network-based adaptive integral terminal sliding mode. Journal of Intelligent & Fuzzy Systems 39 (3):4689–702. doi:10.3233/JIFS-200625.
  • Unguritu, M.-G., and T.-C. Nichitelea. 2023 Design and assessment of an anti-lock braking system controller. Romanian Journal of Information Science and Technology 2023 (1):21–32. Online; Accessed November 27, 2023. doi:10.59277/ROMJIST.2023.1.02.
  • Van Gelder, A., K. A. Ross, and J. S. Schlipf. 1991. The well-founded semantics for general logic programs. Journal of the ACM (JACM) 38 (3):619–49. doi:10.1145/116825.116838.
  • Vennekens, J., S. Verbaeten, and M. Bruynooghe. 2004. Logic programs with annotated disjunctions. In International Conference on Logic Programming, Saint-Malo, France, 431–45. Springer.
  • Wang, K., C. Mu, Z. Ni, and D. Liu. 2023. Safe reinforcement learning and adaptive optimal control with applications to obstacle avoidance problem. IEEE Transactions on Automation Science and Engineering, doi:10.1109/TASE.2023.3299275.
  • Yuan, M., J. Shan, and K. Mi. 2022. Deep reinforcement learning based game-theoretic decision-making for autonomous vehicles. IEEE Robotics and Automation Letters 7 (2):818–25. doi:10.1109/LRA.2021.3134249.
  • Zhang, X., A. Liniger, and F. Borrelli. 2021. Optimization-based collision avoidance. IEEE Transactions on Control Systems Technology 29 (3):972–83. doi:10.1109/TCST.2019.2949540.
  • Zhang, Y., P. Tiňo, A. Leonardis, and K. Tang. 2021. A survey on neural network interpretability. IEEE Transactions on Emerging Topics in Computational Intelligence 5 (5):726–42. doi:10.1109/TETCI.2021.3100641.
  • Zhu, W., Y. Lu, Y. Zhang, X. Wei, and Z. Wei. 2022. End-to-end driving model based on deep learning and attention mechanism. Journal of Intelligent & Fuzzy Systems 42 (4):3337–48. doi:10.3233/JIFS-211206.