On-ramp is one of the scenarios most likely to cause traffic accidents and congestion. In order to mitigate the conflict between mainline vehicles and ramp vehicles, and improve vehicle safety and travel efficiency, we propose an interactive on-ramp merging decision-making (IORMDM) method based on motion prediction. We first propose a motion prediction method for different actions of vehicles based on model predictive control. Meanwhile, the game theory is employed to construct the interaction behavior between the ramp vehicle and the mainline vehicle, and the merging motivation of the ramp vehicle is modeled to describe the collision risk between it with the end of the acceleration lane. Afterward, according to the predicted trajectories of vehicles, the comprehensive payoff of vehicle collision risk, efficiency, passenger comfort, and merging motivation of each action is solved. The Nash equilibrium solution is solved to determine the optimal action of each vehicle according to the payoff table. Simulation experiments demonstrate that our proposed method could reduce collision risk and improve traffic efficiency.
MengXCassandrasCG. Eco-driving of autonomous vehicles for nonstop crossing of signalized intersections. IEEE Trans Autom Sci Eng2022; 19(1): 320–331.
2.
LiLZhaoWXuC, et al. Lane-change intention inference based on RNN for autonomous driving on highways. IEEE Trans Veh Technol2021; 70(6): 5499–5510.
3.
SeilerKMPalmerAWHillAJ. Flow-achieving online planning and dispatching for continuous transportation with autonomous vehicles. IEEE Trans Autom Sci Eng2022; 19(1): 457–472.
4.
LetterCElefteriadouL. Efficient control of fully automated connected vehicles at freeway merge segments. Transp Res Part C Emerg Technol2017; 80: 190–205.
5.
HuZHuangJYangZ, et al. Embedding robust constraint-following control in cooperative on-ramp merging. IEEE Trans Veh Technol2021; 70(1): 133–145.
6.
JingSHuiFZhaoX, et al. Cooperative game approach to optimal merging sequence and on-ramp merging control of connected and automated vehicles. IEEE Trans Intell Transp Syst2019; 20(11): 4234–4244.
7.
XuCZhaoWLiuJ, et al. An integrated decision-making framework for highway autonomous driving using combined learning and rule-based algorithm. IEEE Trans Veh Technol2022; 71: 3621–3632.
8.
ItoYKamalMASYoshimuraT, et al. Coordination of connected vehicles on merging roads using pseudo-perturbation-based broadcast control. IEEE Trans Intell Transp Syst2019; 20(9): 3496–3512.
9.
LiuJZhaoWXuC. An efficient on-ramp merging strategy for connected and automated vehicles in multi-lane traffic. IEEE Trans Intell Transp Syst2022; 23(6): 5056–5067.
10.
WengJXueSYanX. Modeling vehicle merging behavior in work zone merging areas during the merging implementation period. IEEE Trans Intell Transp Syst2016; 17(4): 917–925.
11.
WangYEWTangW, et al. Automated on-ramp merging control algorithm based on Internet-connected vehicles. IET Intell Transp Syst2013; 7(4): 371–379.
12.
Rios-TorresJMalikopoulosAA. Automated and cooperative vehicle merging at highway on-ramps. IEEE Trans Intell Transp Syst2017; 18(4): 780–789.
13.
ChenNvan AremBAlkimT, et al. A hierarchical model-based optimization control approach for cooperative merging by connected automated vehicles. IEEE Trans Intell Transp Syst2021; 22(12): 7712–7725.
14.
HuXSunJ. Trajectory optimization of connected and autonomous vehicles at a multilane freeway merging area. Transp Res Part C Emerg Technol2019; 101: 111–125.
15.
XuHFengSZhangY, et al. A grouping-based cooperative driving strategy for CAVs merging problems. IEEE Trans Veh Technol2019; 68(6): 6125–6136.
16.
WangMHoogendoornSPDaamenW, et al. Game theoretic approach for predictive lane-changing and car-following control. Transp Res Part C Emerg Technol2015; 58: 73–92.
17.
YuHTsengHELangariR. A human-like game theory-based controller for automatic lane changing. Transp Res Part C Emerg Technol2018; 88: 140–158.
18.
KangKRakhaHA. Modeling driver merging behavior: a repeated game theoretical approach. Transp Res Rec J Transp Res Board2018; 2672(20): 144–153.
19.
XuCZhaoWLiL, et al. A Nash Q-learning based motion decision algorithm with considering interaction to traffic participants. IEEE Trans Veh Technol2020; 69(11): 12621–12634.
20.
DingNMengXXiaW, et al. Multivehicle coordinated lane change strategy in the roundabout under Internet of vehicles based on game theory and cognitive computing. IEEE Trans Ind Inform2020; 16(8): 5435–5443.
21.
FukuyamaS. Dynamic game-based approach for optimizing merging vehicle trajectories using time-expanded decision diagram. Transp Res Part C Emerg Technol2020; 120: 102766–102787.
22.
ZhangQLangariRTsengHE, et al. A game theoretic model predictive controller with aggressiveness estimation for mandatory lane change. IEEE Trans Intell Vehicles2020; 5(1): 75–89.
23.
FengJWangCXuC, et al. Active collision avoidance strategy considering motion uncertainty of the pedestrian. IEEE Trans Intell Transp Syst2022; 23(4): 3543–3555.
24.
ZhangLLiBHaoY, et al. A novel simultaneous planning and control scheme of automated lane change on slippery roads. IEEE Trans Intell Transp Syst2022; 23(11): 20696–20706.
25.
WangHWangWYuanS, et al. On social interactions of merging behaviors at highway on-ramps in congested traffic. IEEE Trans Intell Transp Syst2022; 23(8): 11237–11248.
26.
HangPLvCHuangC, et al. Cooperative decision making of connected automated vehicles at multi-lane merging zone: a coalitional game approach. IEEE Trans Intell Transp Syst2022; 23(4): 3829–3841.
27.
XiongBKJiangR. Speed advice for connected vehicles at an isolated signalized intersection in a mixed traffic flow considering stochasticity of human driven vehicles. IEEE Trans Intell Transp Syst2022; 23(8): 11261–11272.
