Considering the complex traffic environment in the future, an interactive strategy between human-machine shared control vehicle (HSCV) and automated vehicles (AVs) based on the adaptive non-cooperative game (NCG) theory is proposed to ensure the safety of vehicles interaction control involving drivers with different characteristics. In HSCV, first, considering the environmental risk potential field (ERPF) and the human-machine conflict coefficient, an adaptive weight distribution method is designed to realize the weight distribution between a driver and an automation system. Subsequently, the steering angle command imposed by a driver is introduced into the game solving process between HSCV and AVs, enabling adaptive dynamic game through weight adjustment. During V2V interaction, based on NCG and distributed model predictive control, a motion planning controller (MOPC) is designed to integrate the optimal solution problem of path planning and tracking control and solve the interaction problem with constraints. Two typical interactive scenarios are used to verify the feasibility of the strategy. The results demonstrate that the interaction strategy considering the different drivers’ operating commands not only realizes the interaction between a driver and an ego vehicle but also realizes the interaction between an ego vehicle and nearby vehicles to ensure the safety of vehicles.
XueWWangZZhengR, et al. Fail-safe behavior and motion planning incorporating shared control for potential driver intervention. IEEE Trans Intell Vehicles2023; 8(7): 3884–3897.
2.
DengHZhaoYFengS, et al. Shared control for intelligent vehicle based on handling inverse dynamics and driving intention. IEEE T Veh Technol2022; 71(3): 2706–2720.
3.
SongDZhuBZhaoJ, et al. Human–machine shared lateral control strategy for intelligent vehicles based on human driver risk perception reliability. Automot Innov2024; 7(1): 102–120.
4.
ZhaoXYinZHeZ, et al. Indirect shared control strategy for Human-machine cooperative driving on hazardous curvy roads. IEEE Trans Intell Vehicles2023; 8(3): 2257–2270.
5.
ChenGGaoZHuaM, et al. Lane change trajectory prediction considering driving style uncertainty for autonomous vehicles. Mech Syst Signal Process2024; 206: 110854
6.
ZhangZWongSWWenY, et al. A full-metal dual-band millimeter-wave antenna array with concomitant multifold orthogonal beamforming for V2V and V2I communications. IEEE T Veh Technol2024; 73(7): 10381–10389.
7.
Lucas-EstanMCColl-PeralesBShimizuT, et al. An analytical latency model and evaluation of the capacity of 5G NR to support V2X services using V2N2V Communications. IEEE T Veh Technol2023; 72(2): 2293–2306.
8.
LiGShiW.Research on lane-changing decision and control of autonomous vehicles based on game theory. P I Mech Eng D-J2024; 0(0): 1–11. https://doi.org/10.1177/09544070241227092
9.
NaXColeDJ.Linear quadratic game and non-cooperative predictive methods for potential application to modelling driver–AFS interactive steering control. Veh Syst Dyn2013; 51(2): 165–198.
10.
LiSWangHLiD, et al. Adaptive authority dynamic game for human-machine cooperative control. P I Mech Eng D-J2023; 0(0): 1–12. doi:10.1177/09544070231182458
11.
HangPHuangCHuZ, et al. Decision making of connected automated vehicles at an unsignalized roundabout considering personalized driving behaviours. IEEE T Veh Technol2021; 70(5): 4051–4064.
12.
TianRLiSLiN, et al. Adaptive game-theoretic decision making for autonomous vehicle control at roundabouts. In: 57th IEEE conference on decision and control (CDC), Miami, FL, 2018, pp. 321−326. New York: IEEE.
13.
HangPLvCHuangC, et al. An integrated framework of decision making and motion planning for autonomous vehicles considering social behaviors. IEEE T Veh Technol2020; 69(12): 14458–14469.
14.
JiXLiuYHeX, et al. Interactive control paradigm-based robust lateral stability controller design for autonomous automobile path tracking with uncertain disturbance: a dynamic game approach. IEEE T Veh Technol2018; 67(8): 6906–6920.
15.
LiMCaoHLiG, et al. A two-layer potential-field-driven model predictive shared control towards driver-automation cooperation. IEEE T Intell Transp2022; 23(5): 4415–4431.
16.
JiangYZhangXXuX, et al. Event-triggered shared lateral control for safe-maneuver of intelligent vehicles. Sci China Inf Sci2021; 64(7): 172203.
17.
JiXYangKNaX, et al. Feedback game-based shared control scheme design for emergency collision avoidance: a fuzzy-linear quadratic regulator approach. J Dyn Syst-T Asme, and Control2019; 141(8): 081005.
18.
LiuJGuoHSongL, et al. Driver-automation shared steering control for highly automated vehicles. Sci China Inf Sci2020; 63(9): 190201.
19.
LiTHanXMaJ, et al. Operational safety of automated and human driving in mixed traffic environments: a perspective of car-following behavior. P I Mech Eng O-J Ris2023; 237(2): 355–366.
20.
ChenS-PXiongG-MChenH-Y, et al. MPC-based path tracking with PID speed control for high-speed autonomous vehicles considering time-optimal travel. J Central South Univ2020; 27(12): 3702–3720.
21.
WolfMBurdickJ. Artificial potential functions for highway driving with collision avoidance. In: 2008 IEEE International Conference on Robotics and Automation (ICRA), Pasadena, CA, 2008, pp. 3731–3736. New York: IEEE.
22.
HongyuHChiZYuhuanS, et al. An improved artificial potential field model considering vehicle velocity for autonomous driving. IFAC-PapersOnLine2018; 51(31): 863–867.
23.
WangJWuJLiY.The Driving safety field based on driver–vehicle–road interactions. IEEE T Intell Transp2015; 16(4): 2203–2214.
24.
LiMSongXCaoH, et al. Shared control with a novel dynamic authority allocation strategy based on game theory and driving safety field. Mech Syst Signal Process2019; 124: 199–216.
25.
ZhangWZhaoYZhangX, et al. Adaptive shared control strategy for lane changing assistance system via multi-mode switching. J Frankl2020; 357(18): 13304–13325.
26.
TavanaMAziziFAziziF, et al. A fuzzy inference system with application to player selection and team formation in multi-player sports. Sport Manag Rev2013; 16(1): 97–110.
27.
HuangCLvCHangP, et al. Human–machine adaptive shared control for safe driving under automation degradation. IEEE Intel Transp Sy2022; 14(2): 53–66.
28.
HuangCLvCNaghdyF, et al. Reference-free approach for mitigating human–machine conflicts in shared control of automated vehicles. IET Control Theory Appl2020; 14(18): 2752–2763.
29.
LuJ.Research on human-machine co-driving control based on model prediction and game theory. Master Thesis, Chongqing Uinversity, 2021.
30.
HajilooRAbroshanMKhajepourA, et al. Integrated steering and differential braking for emergency collision avoidance in autonomous vehicles. IEEE T Intell Transps2021; 22(5): 3167–3178.
31.
ErlienSFujitaSGerdesJ. Safe driving envelopes for shared control of ground vehicles. In: 7th IFAC symposium on advances in automotive control (AAC), Tokyo, Japan, 2013, pp. 831–836. Austria: Elsevier Ltd.
32.
ChenH (ed.). Model predictive control. 1st ed. Beijing: Science Press, 2013, p. 181.
33.
DasDBanerjeeSChatterjeeP, et al. A secure blockchain enabled V2V communication system using smart contracts. IEEE T Intell Transp2023; 24(4): 4651–4660.
34.
ZhangKWangJChenN, et al. A non-cooperative vehicle-to-vehicle trajectory-planning algorithm with consideration of driver’s characteristics. P I Mech Eng D-J2019; 233(10): 2405–2420.
