To ensure the safety and stability of vehicles during human-machine collaborative driving, this paper proposes a multi-factor optimization-based authority allocation strategy to address the distribution of control between the driver and the automation system. First, to minimize the impact of human-machine conflicts on the vehicle, we design a driving intention consistency allocation strategy model (DICS) using a cosine similarity algorithm. Next, we develop a driver’s driving ability-based authority allocation strategy (DDAS) using the Hamiltonian Monte Carlo algorithm, which allocates control based on an evaluation of the driver’s driving ability. Additionally, a spatial collision risk-based authority allocation model (SCRM) is established using Artificial Potential Field (APF) and Dynamic Potential Field (DPF) methods to assess the impact of environmental conditions on authority allocation. Finally, a fuzzy inference algorithm is adopted to dynamically perform comprehensive internal allocation, with its scope determined by the allocation values corresponding to DICS, DDAS, and SCRM. Simulation results demonstrate that the proposed strategy effectively coordinates human-machine conflict, driver capability, and environmental risk, enhancing vehicle safety and stability during collaborative driving. The crucial code can be obtained from the provided link: https://github.com/gyhhq/human-machine-cooperative-driving.
SentouhCNguyenATBenloucifMA, et al. Driver-automation cooperation oriented approach for shared control of lane keeping assist systems. IEEE Trans Control Syst Technol2019; 27(5): 1962–1978.
2.
vanHoekRPloegJNijmeijerH.Cooperative driving of automated vehicles using B-splines for trajectory planning. IEEE Trans Intell Vehicles2021; 6(3): 594–604.
3.
LiZGongCLinY, et al. Continual driver behaviour learning for connected vehicles and intelligent transportation systems: framework, survey and challenges. Green Energy and Intelligent Transportation2023; 2(4): 100103.
4.
LiMJiangCSongX, et al. Parameter effects of the potential-field-driven model predictive controller for shared control. Automot Innov2022; 6(1): 48–61.
5.
TerkenJPflegingB.Toward shared control between automated vehicles and users. Automot Innov2020; 3(1): 53–61.
6.
WangWNaXCaoD, et al. Decision-making in driver-automation shared control: a review and perspectives. IEEE/CAA J Autom Sin2020; 7(5): 1289–1307.
7.
FlemischFAbbinkDItohM, et al. Shared control is the sharp end of cooperation: towards a common framework of joint action, shared control and human machine cooperation. IFAC-PapersOnLine2016; 49(19): 72–77.
8.
LiMCaoHLiG, et al. A two-layer potential-field-driven model predictive shared control towards driver-automation cooperation. IEEE Trans Intell Transp Syst2022; 23(5): 4415–4431.
9.
HanJZhaoJZhuB, et al. Adaptive steering torque coupling framework considering conflict resolution for human-machine shared driving. IEEE Trans Intell Transp Syst2022; 23(8): 10983–10995.
10.
GuoHShiWLiuJ, et al. Game-theoretic human-machine shared steering control strategy under extreme conditions. IEEE Trans Intell Vehicles2024; 9: 2766–2779.
11.
JiXYangKNaX, et al. Shared steering torque control for lane change assistance: a stochastic game-theoretic approach. IEEE Trans Ind Electron2019; 66(4): 3093–3105.
12.
HuangCHangPHuZ, et al. Collision-probability-aware human-machine cooperative planning for safe automated driving. IEEE Trans Vehicular Technol2021; 70(10): 9752–9763.
13.
HuangCHuangHZhangJ, et al. Human-machine cooperative trajectory planning and tracking for safe automated driving. IEEE Trans Intell Transp Syst2022; 23(8): 12050–12063.
14.
TianYZhaoYShiY, et al. The indirect shared steering control under double loop structure of driver and automation. IEEE/CAA J Autom Sin2020; 7: 1403–1416.
15.
ItohMPacaux-LemoineMP.Trust view from the human-machine cooperation framework. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018, pp.3213–3218. Japan: IEEE.
16.
ShiZChenHQuT, et al. Human–machine cooperative steering control considering mitigating human–machine conflict based on driver trust. IEEE Trans Hum Mach Syst2022; 52(5): 1036–1048.
17.
NguyenATSentouhCPopieulJC.Sensor reduction for driver-automation shared steering control via an adaptive authority allocation strategy. IEEE/ASME Trans Mechatron2018; 23(1): 5–16.
18.
ZhouSJuZLiuY, et al. Driver state detection for driver-automation shared control with fuzzy logic. Control Eng Pract2022; 127: 105294.
19.
LiZChenLNieL, et al. A novel learning model of driver fatigue features representation for steering wheel angle. IEEE Trans Vehic Technol2022; 71(1): 269–281.
20.
Muhlbacher-KarrerSMosaAHFallerLM, et al. A driver state detection system—combining a capacitive hand detection sensor with physiological sensors. IEEE Trans Instrum Meas2017; 66(4): 624–636.
21.
MarcanoMDiazSPerezJ, et al. A review of shared control for automated vehicles: theory and applications. IEEE Trans Hum Mach Syst2020; 50(6): 475–491.
22.
YueMFangCZhangH, et al. Adaptive authority allocation-based driver-automation shared control for autonomous vehicles. Accid Anal Prev2021; 160: 106301.
23.
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.
24.
WangZGongXLiX, et al. Human–machine shared steering control under high-speed emergency obstacle avoidance scenarios. Transp Res Rec2024; 2678: 70–85.
25.
JiangYZhangXXuX, et al. Event-triggered shared lateral control for safe-maneuver of intelligent vehicles. Sci China Inf Sci2021; 64(7): 172203.
26.
ZhangZWangCZhaoW, et al. Driving authority allocation strategy based on driving authority real-time allocation domain. IEEE Trans Intell Transp Syst2022; 23(7): 8528–8543.
27.
GuoWTengZSongX, et al. Toward consumer acceptance of cooperative driving systems: a human-centered shared steering control approach within a hierarchical framework. IEEE Trans Consum Electron2024; 70: 635–645.
28.
HasanFHuangH.Driver intention and interaction-aware trajectory forecasting via modular multi-task learning. IEEE Trans Consum Electron2024; 70: 1857–1865.
29.
MaBLiuYNaX, et al. A shared steering controller design based on steer-by-wire system considering human-machine goal consistency. J Franklin Inst2019; 356(8): 4397–4419.
30.
HanLYuanHXuW, et al. Modified dynamic movement primitives: robot trajectory planning and force control under curved surface constraints. IEEE Trans Cybern2023; 53(7): 4245–4258.
31.
NguyenATSentouhCPopieulJC.Driver-automation cooperative approach for shared steering control under multiple system constraints: design and experiments. IEEE Trans Ind Electron2017; 64(5): 3819–3830.
32.
LiuFXueFWangW, et al. Real-time comprehensive driving ability evaluation algorithm for intelligent assisted driving. Green Energy Intelligent Transportation2023; 2(2): 100065.
33.
SuWXueFWenY, et al. Real-time driving ability evaluation algorithm for Human-machine co-driving decision. J Northeast Univ2023; 44(8): 1078.
34.
FangLTian-heZWei-XingS, et al. Regionalized decision algorithm for human-machine shared control based on Gaussian hidden Markov model. Acta Electron Sin2022; 50(11): 2659–2667.
35.
NagarLFernández-PendásMSanz-SernaJM, et al. Adaptive multi-stage integration schemes for Hamiltonian Monte Carlo. J Comput Phys2024; 502: 112800.
36.
HoffmanMDGelmanA.The no-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J Mach Learn Res2014; 15(1): 1593–1623.
37.
MestryDVBhowmickAR.Demystifying Monte Carlo methods in R: a guide from Metropolis–Hastings to Hamiltonian Monte Carlo with biological growth equation examples. Ecol Modell2025; 501: 110922.
38.
BenkerMFurtnerLSemmT, et al. Utilizing uncertainty information in remaining useful life estimation via Bayesian neural networks and Hamiltonian Monte Carlo. J Manuf Syst2021; 61: 799–807.
39.
GaoHZhuJZhangT, et al. Situational assessment for intelligent vehicles based on stochastic model and Gaussian distributions in typical traffic scenarios. IEEE Trans Syst Man Cybern Syst2022; 52(3): 1426–1436.
40.
GuanYLiNChenP, et al. Human–machine cooperative vehicle control based on driving intention and risk avoidance. Int J Automot Technol2025; 26: 877–894.
41.
RostamiSMHSangaiahAKWangJ, et al. Obstacle avoidance of mobile robots using modified artificial potential field algorithm. EURASIP J Wirel Commun Netw2019; 2019(1): 1–19.
42.
LiLGanJJiX, et al. Dynamic driving risk potential field model under the connected and automated vehicles environment and its application in car-following modeling. IEEE Trans Intell Transp Syst2022; 23(1): 122–141.
43.
WangJWuJLiY.The driving safety field based on driver–vehicle–road interactions. IEEE Trans Intell Transp Syst2015; 16(4): 2203–2214.
44.
WangSLiZWangB, et al. Velocity obstacle-based collision avoidance and motion planning framework for connected and automated vehicles. Transp Res Rec2022; 2676(5): 748–766.
45.
YeqiangLFajuQJianghuiX, et al. Dynamic obstacle avoidance for path planning and control on intelligent vehicle based on the risk of collision. WSEAS Trans Syst Contr2013; 3(12): 154–164.
46.
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.
47.
MamdaniEHAssilianS.An experiment in linguistic synthesis with a fuzzy logic controller. Int J Mach Stud1975; 7(1): 1–13.
48.
WuJKongQYangK, et al. Research on the steering torque control for intelligent vehicles co-driving with the penalty factor of human–machine intervention. IEEE Trans Syst Man Cybern Syst2023; 53(1): 59–70.
49.
XingXLiWYuanS, et al. Fuzzy logic-based arbitration for shared control in continuous human–robot collaborationIEEE Trans Fuzzy Syst2024; 32: 3979–3991.
50.
GuanYLiNChenP, et al. Research on path tracking control based on optimal look-ahead points. Int J Autom Technol2024; 25: 1355–1374.
