To assure safety and comfort for autonomous vehicles (AVs) in the mixed traffic environment, a longitudinal potential field-based car-following model considering the cut-in behavior from an adjacent vehicle is established in this study. The longitudinal potential field (LPF), which includes the cut-in potential field, the hazard potential field and the velocity potential field, is constructed to quantitatively characterize the influence of the surrounding environment on the AVs. The cut-in potential field is created to indicate the possibility of cut-in behavior from the adjacent vehicle, the hazard potential field is intended to depict the risk of colliding with the preceding vehicle, and the velocity potential field is created to show how velocity impacts the following behavior. The proposed model is obtained by calculating the field force in the LPF, and its parameters are calibrated using the genetic algorithm. Comparative simulations are executed to verify the remarkable performance of the proposed model, in terms of acceleration, velocity, and headway tracking as well as responding to the cut-in behavior under three different driving conditions.
LiTNgoduyDHuiF, et al. A car-following model to assess the impact of V2V messages on traffic dynamics. Transportmetrica B Transp Dyn2020; 8(1): 150–165.
2.
ZhuWXZhangHM. Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model. Physica2018; 496: 274–285.
3.
GippsPG. A behavioural car-following model for computer simulation. Transp Res Part B Methodol1981; 15(2): 105–111.
4.
LiYZhangLPeetaS, et al. Non-lane-discipline-based car-following model considering the effects of two-sided lateral gaps. Nonlinear Dyn2015; 80: 227–238.
5.
ChandlerREHermanRMontrollEW. Traffic dynamics: studies in car following. Oper Res1958; 6(2): 165–184.
6.
SomdaFHCormeraisH. Auto-adaptive and string stable strategy for intelligent cruise control. IET Intell Transp Syst2011; 5(3): 168–174.
7.
BandoMHasebeKNakayamaA, et al. Dynamical model of traffic congestion and numerical simulation. Phys Rev E1995; 51(2): 1035–1042.
8.
JiangRWuQZhuZ. Full velocity difference model for a car-following theory. Phys Rev E2001; 64(1): 017101.
9.
El MajdoubKGiriFOuadiH, et al. Vehicle longitudinal motion modeling for nonlinear control. Control Eng Pract2012; 20(1): 69–81.
10.
SwaroopDRajagopalKR. A review of constant time headway policy for automatic vehicle following. In: IEEE Transp. Intell. Syst. Proceedings (Cat. No. 01TH8585), 2001; pp.65–69.
11.
LiTChenDZhouH, et al. Car-following behavior characteristics of adaptive cruise control vehicles based on empirical experiments. Transp Res Part B Methodol2021; 147: 67–91.
12.
LiuHKanXShladoverSE, et al. Modeling impacts of cooperative adaptive cruise control on mixed traffic flow in multi-lane freeway facilities. Transp Res Pt C-Emerg Technol2018; 95: 261–279.
13.
GunterGJanssenCBarbourW, et al. Model-based string stability of adaptive cruise control systems using field data. IEEE Trans Intell Vehicles2020; 5(1): 90–99.
14.
MakridisMMattasKAnesiadouA, et al. OpenACC. An open database of car-following experiments to study the properties of commercial ACC systems. Transp Res Pt C-Emerg Technol2021; 125: 103047.
15.
WangHQinYWangW, et al. Stability of CACC-manual heterogeneous vehicular flow with partial CACC performance degrading. Transportmetrica B Transp Dyn2019; 7(1): 788–813.
16.
GhiasiALiXMaJ. A mixed traffic speed harmonization model with connected autonomous vehicles. Transp Res Pt C-Emerg Technol2019; 104: 210–233.
17.
AmirgholyMShahabiMOliver GaoH. Traffic automation and lane management for communicant, autonomous, and human-driven vehicles. Transp Res Pt C-Emerg Technol2020; 111: 477–495.
18.
WangXYangMHurwitzD. Analysis of cut-in behavior based on naturalistic driving data. Accid Anal Prev2019; 124: 127–137.
19.
WuXQinGYuH, et al. Using improved chaotic ant swarm to tune PID controller on cooperative adaptive cruise control. Optik2016; 127(6): 3445–3450.
20.
HartwichFBeggiatoMKremsJF. Driving comfort, enjoyment and acceptance of automated driving - effects of drivers’ age and driving style familiarity. Ergonomics2018; 61(8): 1017–1032.
21.
FuRLiZSunQ, et al. Human-like car-following model for autonomous vehicles considering the cut-in behavior of other vehicles in mixed traffic. Accid Anal Prev2019; 132: 105260.
22.
ZhangYLinQWangJ, et al. Lane-change intention estimation for car-following control in autonomous driving. IEEE Trans Intell Vehicles2018; 3(3): 276–286.
23.
KazemiHMahjoubHNTahmasbi-SarvestaniA, et al. A learning-based stochastic MPC design for cooperative adaptive cruise control to handle interfering vehicles. IEEE Trans Intell Vehicles2018; 3(3): 266–275.
24.
DouYNiDWangZ, et al. Strategic car-following gap model considering the effect of cut-ins from adjacent lanesIET Intell Transp Syst2016; 10(10): 658–665.
25.
WangJWuJLiY. The driving safety field based on driver–Vehicle–Road Interactions. IEEE Trans Intell Transp Syst2015; 16(4): 2203–2214.
26.
WangJWuJZhengX, et al. Driving safety field theory modeling and its application in pre-collision warning system. Transp Res Pt C-Emerg Technol2016; 72: 306–324.
27.
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.
28.
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.
29.
TanHLuGLiuM. Risk field model of driving and its application in modeling car-following behavior. IEEE Trans Intell Transp Syst2022; 23(8): 11605–11620.
30.
HeJKingMWatsonB, et al. Speed enforcement in China: national, provincial and city initiatives and their success. Accid Anal Prev2013; 50: 282–288.
31.
FengFBaoSSayerJR, et al. Can vehicle longitudinal jerk be used to identify aggressive drivers? An examination using naturalistic driving data. Accid Anal Prev2017; 104: 125–136.
32.
ChakrobortyPKikuchiS. Evaluation of the General Motors based car-following models and a proposed fuzzy inference model. Transp Res Pt C-Emerg Technol1999; 7(4): 209–235.
33.
ZengYZZhangN. New full velocity difference model considering the driver’s heterogeneity of the disturbance risk preference for car-following theory. Int J Mod Phys B2016; 30(01): 1550241.
