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Review and Outlook of Decision-Making Methods in Unmanned Ground Vehicles

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Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) (ICAUS 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 861))

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Abstract

This paper provide an overview of the emerging trends and challenges in the field of decision-making of unmanned ground vehicles. Specifically, four important decision-making methods are analyzed, including the classical rule-based, the decision tree based, reinforcement learning based, and POMDP based decision-making system. Furthermore, we compare the pros and cons among these methods, and analyze the difficulties encountered in current decision-making systems. On the basis of these comparison and analysis, we suggest that the integration of different methods could improve the performance of the decision-making systems. Finally, we discuss the future development for the decision-making system and recommend to combine graph-based knowledge representation and learning-based method to tackle the complex task and traffic information in the decision-making process.

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References

  1. Schwarting, W., Alonso-Mora, J., Rus, D.: Planning and decision-making for autonomous vehicles. Ann. Rev. Control Robot. Auton. Syst. 1, 187–210 (2018)

    Article  Google Scholar 

  2. Ramanathan, P., Kartik.: Autonomous driving cars: decision-making. In: Gupta N., Prakash A., Tripathi R. (eds.) Internet of Vehicles and its Applications in Autonomous Driving. Unmanned System Technologies. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-46335-9_3

  3. **ong, L., Kang, Y., Zhang, P., Zhu, C., Yu, Z.: Research on behavior decision-making system for unmanned vehicle. Automobile Technol. 8, 1–9 (2018)

    Google Scholar 

  4. Geng, X.: Research on behavior decision-making approaches for autonomous vehicle in urban uncertainty. University of Science and Technology of China (2017)

    Google Scholar 

  5. Niehaus, A., Stengel, R.F.: Probability-based decision making for automated highway driving. IEEE Trans. Veh. Technol. 43, 626–634 (1994)

    Article  Google Scholar 

  6. Urmson, C., et al.: Autonomous driving in urban environments: boss and the urban challenge. J. Field Robot. 25(8), 425–466 (2008)

    Article  Google Scholar 

  7. Sun, Z.: An intelligent control system for autonomous land vehicle. Dissertation, National University of Defense Technology (2004)

    Google Scholar 

  8. Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H.: Junior: the Stanford entry in the urban challenge. J. Field Robot. 25, 569–597 (2008)

    Article  Google Scholar 

  9. Patz, B.J., Papelis, Y., Pillat, R., Stein, G., Harper, D.: A practical approach to robotic design for the DARPA urban challenge. J. Field Robot. 25, 528–566 (2008)

    Article  Google Scholar 

  10. Du, M.: Research on behavior decision-making and motion planning methods of autonomous vehicle based on human driving behavior. University of Science and Technology of China (2016)

    Google Scholar 

  11. Wang, X., Yang, X.: Study on decision mechanism of driving behavior based on decision tree. J. Syst. Simul. 2,415–419+448 (2008)

    Google Scholar 

  12. Liu, K., Li, J.: Analysis of individual driving behavior based on decision tree C4.5 algorithm. Comput. Eng. Softw. 6, 83–86 (2016)

    Google Scholar 

  13. Zhang, Y., Haghani, A.: A gradient boosting method to improve travel time prediction. Trans. Res. Part C Emerg. Technol. 58, 308–324 (2015)

    Article  Google Scholar 

  14. Hou, Y., Edara, P., Sun, C.: Modeling mandatory lane changing using bayes classifier and decision trees. IEEE Trans. Intell. Transp. Syst. 15(2), 647–655 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Carreras, M., Yuh, J., Batlle, J., Ridao, P.: A behavior-based scheme using reinforcement learning for autonomous underwater vehicles. IEEE J. Oceanic Eng. 30, 416–427 (2005)

    Article  Google Scholar 

  17. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M.: Playing Atari with deep reinforcement learning. ar** assist. ar**v preprint ar**v:1612.04340 (2016)

  18. Chae, H., Kang, C.M., Kim, B., Kim, J., Chung, C.C., Choi, J.W.: Autonomous braking system via deep reinforcement learning. In: IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1–6. IEEE (2017)

    Google Scholar 

  19. Sallab, A., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. Electron. Imaging 2017, 70–76 (2017)

    Article  Google Scholar 

  20. Spryn, M., Sharma, A., Parkar, D., Shrimal, M.: Distributed deep reinforcement learning on the cloud for autonomous driving. In: 2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS), Gothenburg, Sweden (2018)

    Google Scholar 

  21. Min, K., Kim, H., Huh, K.: Deep q learning based high level driving policy determination. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 226–231. Changshu, China (2018)

    Google Scholar 

  22. Zhang, B., He, M., Chen, X.: Self-driving via improved DDPG algorithm. Comput. Eng. Appl. 55(10), 264–270 (2019)

    Google Scholar 

  23. Huang, Z., Zhang, J., Tian, R.: End-to-end autonomous driving decision based on deep reinforcement learning. In: 2019 5th International Conference on Control, Automation and Robotics (ICCAR), pp. 658–662. Bei**g, China (2019)

    Google Scholar 

  24. Chu, T., Wang, J., Codecà, L., Li, Z.: Multi-agent deep reinforcement learning for large-scale traffic signal control. IEEE Trans. Intell. Transp. Syst. 21(3), 1086–1095 (2020)

    Article  Google Scholar 

  25. Peixoto, J.P.M., Azim, A.: Context-based learning for autonomous vehicles. In: 2020 IEEE 23rd International Symposium on Real-Time Distributed Computing (ISORC), pp. 150–151 (2020)

    Google Scholar 

  26. Cao, Z., Yang, D., Xu, S.: Highway exiting planner for automated vehicles using reinforcement learning. IEEE Trans. Intell. Transp. Syst. 22(2), 990–1000 (2021)

    Article  Google Scholar 

  27. Mo, S., Pei, X., Wu, C.: Safe reinforcement learning for autonomous vehicle using monte Carlo tree search. IEEE Trans. Intell. Transp. Syst. 1–8 (2021)

    Google Scholar 

  28. Brechtel, S., Gindele, T., Dillmann, R.: Probabilistic decision-making under uncertainty for autonomous driving using continuous POMDPs. In: 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), pp. 392–99. IEEE, New York (2014)

    Google Scholar 

  29. Liu, W., Kim, S.W., Pendleton, S., Ang, M.H.: Situation-aware decision making for autonomous driving on urban road using online POMDP. In: 2015 IEEE Intelligent Vehicles Symposium (IV), pp. 1126–33.IEEE, New York (2015)

    Google Scholar 

  30. Gindele, T., Brechtel, S., Dillmann, R.: Learning driver behavior models from traffic observations for decision making and planning. IEEE Intell. Transp. Syst. Mag. 7(1), 69–79 (2015)

    Article  Google Scholar 

  31. Galceran, E., Cunningham, A.G., Eustice, R.M., Olson, E.: Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment. Auton. Robot. 41(6), 1367–1382 (2017). https://doi.org/10.1007/s10514-017-9619-z

    Article  Google Scholar 

  32. Zhou, B., Schwarting, W., Rus, D., Alonso-Mora, J.: Joint multi-policy behavior estimation and receding-horizon trajectory planning for automated urban driving. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2388–2394. Brisbane, QLD, Australia (2018)

    Google Scholar 

  33. Pouya, P., Madni, A.M.: Expandable-partially observable Markov decision-process framework for modeling and analysis of autonomous vehicle behavior. IEEE Syst. J. 15(3), 3714–3725 (2021)

    Google Scholar 

  34. Chen, P.: Rearch on key Technology of Control and Decision in Driver Assistant System. Dissertation, Shanghai Jiao Tong University (2011)

    Google Scholar 

  35. Huang, Z.: The Modeling and Simulation of Longitudinal Platoon-cooperation Maneuvers Based on FEM. Dissertation, Wuhan University of Technology (2013)

    Google Scholar 

  36. Gong, J., Yuan, S., Yan, J., Chen, X., Di, H.: Intuitive decision-making modeling for self-driving vehicles. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 29–34. IEEE (2014)

    Google Scholar 

  37. Zhang, S.: Research on Knowledge Base Management System for the Behavior Decision of Unmanned Vehicle. Dissertation, University of Science and Technology of China (2020)

    Google Scholar 

  38. Wang, S., Zhang, Y., Liao, Z.: Building domain-specific knowledge graph for unmanned combat vehicle decision making under uncertainty. In: 2019 Chinese Automation Congress (CAC), pp. 4718–4721. Hangzhou, China (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. College of Liberal Arts and Sciences, National University of Defense Technology, Changsha, China

    Lina Qin & Aihua Yang

  2. College of Intelligence Sciences and Technology, National University of Defense Technology, Deya Road 109, Changsha, Hunan, China

    Junxiang Li & Liu Liu

  3. School of Mechanical Engineering, **angtan University, **angtan, Hunan, China

    Yanlin Li

  4. Inner Mongolia First Machinery Group Co., Ltd, Baotou, Inner Mongolia, China

    Yu Feng

Authors
  1. Lina Qin
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  2. Aihua Yang
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  3. Junxiang Li
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  4. Yanlin Li
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  5. Yu Feng
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  6. Liu Liu
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Corresponding author

Correspondence to Junxiang Li .

Editor information

Editors and Affiliations

  1. College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China

    Mei** Wu

  2. College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China

    Yifeng Niu

  3. Bei**g HIWING Scientific and Technological Information Institute, Bei**g, China

    Mancang Gu

  4. Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory, Bei**g, China

    ** Cheng

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Qin, L., Yang, A., Li, J., Li, Y., Feng, Y., Liu, L. (2022). Review and Outlook of Decision-Making Methods in Unmanned Ground Vehicles. In: Wu, M., Niu, Y., Gu, M., Cheng, J. (eds) Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). ICAUS 2021. Lecture Notes in Electrical Engineering, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-16-9492-9_287

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