Research on driving behavior decision making system of autonomous driving vehicle based on benefit evaluation model

• Autorzy: Wang Pengwei , Gao Song , Li Liang , Cheng Shuo , Zhao Hailan

Identyfikatory

DOI

10.5604/01.3001.0014.1740

• Języki publikacji: EN

Abstrakty

EN

Autonomous driving vehicle could increase driving efficiency, reduce traffic congestion and improve driving safety, it is considered as the solution of current traffic problems. Decision making systems for autonomous driving vehicles have significant effects on driving performance. The performance of decision making system is affected by its framework and decision making model. In real traffic scenarios, the driving condition of autonomous driving vehicle faced is random and time-varying, the performance of current decision making system is unable to meet the full scene autonomous driving requirements. For autonomous driving vehicle, the division between different driving behaviors needs clear boundary conditions. Typically, in lane change scenario, multiple reasonable driving behavior choices cause conflict of driving state. The fundamental cause of conflict lies in overlapping boundary conditions. To design a decision making system for autonomous driving vehicles, firstly, based on the decomposition of human driver operation process, five basic driving behavior modes are constructed, a driving behavior decision making framework for autonomous driving vehicle based on finite state machine is proposed. Then, to achieve lane change decision making for autonomous driving vehicle, lane change behavior characteristics of human driver lane change maneuver are analyzed and extracted. Based on the analysis, multiple attributes such as driving efficiency and safety are considered, all attributes benefits are quantified and the driving behavior benefit evaluation model is established. By evaluating the benefits of all alternative driving behaviors, the optimal driving behavior for current driving scenario is output. Finally, to verify the performances of the proposed decision making model, a series of real vehicle tests are implemented in different scenarios, the real time performance, effectiveness, and feasibility performance of the proposed method is accessed. The results show that the proposed driving behavior decision making model has good feasibility, real-time performance and multi-choice filtering performance in dynamic traffic scenarios.

Słowa kluczowe

EN

autonomous driving vehicle; decision making model; finite state machine; lane change decision; driving performance

PL

pojazd autonomiczny; model podejmowania decyzji; zmiana pasa ruchu; wydajność jazdy

• Wydawca: Warsaw University of Technology, Faculty of Transport
• Czasopismo: Archives of Transport
• Rocznik: 2020
• Tom: Vol. 53, iss. 1
• Strony: 21--36
• Opis fizyczny: Bibliogr. 27 poz., rys., wykr., wzory

Twórcy

autor

Wang Pengwei

• School of Transportation and Vehicle Engineering, Shandong University of Technology, China

autor

Gao Song

• School of Transportation and Vehicle Engineering, Shandong University of Technology, China

autor

Li Liang

• State Key Laboratory of Automotive Safety and Energy, Tsinghua University, China

autor

Cheng Shuo

• State Key Laboratory of Automotive Safety and Energy, Tsinghua University, China

autor

Zhao Hailan

• Zibo Vocational Institute, China

Bibliografia

• [1] CHEN, H., XIONG, G., GONG, J., 2014. Introduction to self-driving car. Beijing: Beijing Institute of Technology Press.
• [2] CZECH, P., TUROŃ, K., BARCIK, J., 2018. Autonomous vehicles: basic issues. Scientific Journal of Silesian University of Technology. Series Transport. Vol. 100, pp. 15-22.
• [3] XIONG, L., KANG, Y., ZHANG, P., ZHU, C., YU, Z., 2018. Research on Behavior Decision-making System for Unmanned Vehicle. Automobile Technology, 515(8): 4-12.
• [4] VERES, S, M., MOLNAR, L., LINCOLN, N., K., ET AL., 2011. Autonomous vehicle control systems - A review of decision making. Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems & Control Engineering, 225(3):155-195.
• [5] CHENG, S., LI, L., GUO, H., ET AL., 2019. Longitudinal Collision Avoidance and Lateral Stability Adaptive Control System Based on MPC of Autonomous Vehicles. IEEE Transactions on Intelligent Transportation Systems, 10(11): 1737 PP(99):1-10.
• [6] WU, J., CHENG, S., LIU, B., ET AL., 2017. A Human-Machine-Cooperative-Driving Controller Based on AFS and DYC for Vehicle Dynamic Stability. Energies, 10(11): 1737.
• [7] GALCERAN, E., CUNNINGHAM, A, G., EUSTICE, R, M., ET AL., 2017. Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment. Autonomous Robots, 41(6):1367-1382.
• [8] CUNNINGHAM, A, G., GALCERAN, E., EUSTICE, R, M., ET AL., 2015. MPDM: Multipolicy decision-making in dynamic, uncertain environments for autonomous driving. 2015 IEEE International Conference on Robotics and Automation, Seattle, WA, 2015, pp. 1670-1677.
• [9] XIE, M., CHEN, H., ZHANG, X., ET AL., 2007. Development of Navigation System for Autonomous Vehicle to Meet the DARPA Urban Grand Challenge. IEEE Intelligent Transportation Systems Conference. Bellevue, America, 2010.
• [10] FURDA, A., VLACIC, L., 2010. An object-oriented design of a World Model for autonomous city vehicles. Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium, San Diego, CA, 2010, pp. 1054-1059.
• [11] FURDA, A., VLACIC, L., 2010. Multiple Criteria-Based Real-Time Decision Making by Autonomous City Vehicles. 7th IFAC Symposium on Intelligent Autonomous Vehicles. Lecce, Italy. Vol. 43(16):97-102.
• [12] FURDA, A., VLACIC, L., 2010. Real-Time Decision Making for Autonomous City Vehicles. Journal of Robotics and Mechatronics, 22(6): 694.
• [13] CHEN, J., ZHAO, P., LIANG, H., ET AL., 2014. A Multiple Attribute-Based Decision Making Model for Autonomous Vehicle in Urban Environment. 2014 IEEE Intelligent Vehicles Symposium Proceedings, Dearborn, MI, 2014, pp. 480-485.
• [14] JI, J., HUANG, Y., LI, Y., WU, F., 2018. Decision Making Analysis of Autonomous Driving Behaviors for Intelligent Vehicles Based on Finite State Machine. Automobile Technology, Vol. (12), 1-7.
• [15] DU, M., 2016. Research on Behavioral Decision Making and Motion Planning Methods of Autonomous Vehicle Based on Human Driving Behavior. Ph.D. Dissertation, University of Science and Technology of China, Hefei, China.
• [16] XIONG, G., LI, Y., WANG, S., 2015. A behavior prediction and control method based on FSM for intelligent vehicles in an intersection. Transactions of Beijing Institute of Technology, Vol. 35, No, 1, 34-38.
• [17] CZUBENKO, M., KOWALCZUK, Z., ORDYS, A., 2015. Autonomous Driver Based on an Intelligent System of Decision-Making. Cognitive Computation, 7(5):569-581.
• [18] SONG, W., 2016. Research on behavioral decision making for intelligent vehicles in dynamic urban environments. Ph. D. Dissertation, Beijing Institute of Technology, Beijing, China.
• [19] MUSLIM, N. H. B., SHAFAGHAT, A., KEYVANFAR, A., ISMAIL, M., 2018. Green Driver: driving behaviors revisited on safety. Archives of Transport, 47(3), 49-78.
• [20] AFANASIEVA, I., GALKIN, A., 2018. Assessing the information flows and established their effects on the results of driver’s activity. Archives of Transport, 45(1), 7-23.
• [21] XING, Y., LV, C., WANG, H., ET AL., 2019. Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges. IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 4377-4390.
• [22] LIU, Y., WANG, X., LI, L., CHENG, S., CHEN, Z., 2019. A Novel Lane Change Decision-Making Model of Autonomous Vehicle Based on Support Vector Machine. IEEE Access, (7): 26543-26550.
• [23] XIONG, G., KANG, Z., LI, H., SONG, W., JIN, Y., GONG, J., 2018. Decision - making of Lane Change Behavior Based on RCS for Automated Vehicles in the Real Environment. 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, Suzhou, China, June 26-30.
• [24] WANG, P., GAO, S., LI, L., SUN, B., CHENG, S, 2019. Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm. Energies, 12. 2342.
• [25] XING, Y., LV, C., WANG, H., CAO, D., ET AL., 2019. Driver Activity Recognition for Intelligent Vehicles: A Deep Learning Approach. IEEE Transactions on Vehicular Technology, vol. 68, no. 6, pp. 5379-5390.
• [26] LI, S., XU, S., WANG, W., ET AL., 2014. Overview of ecological driving technology and application for ground vehicles. Journal of Automotive Safety and Energy, Vol. 5, (2):121-131.
• [27] LI, E, S., PENG, H., ET AL., 2012. Minimum Fuel Control Strategy in Automated Car-Following Scenarios. IEEE Transactions on Vehicular Technology, vol. 61, no. 3, pp. 998-1007.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020)