Abstract
In a cut-in scenario, traditional adaptive cruise control usually cannot effectively identify the cut-in vehicle and respond to it in advance. This paper proposes an adaptive cruise control (ACC) strategy based on the MPC algorithm for cut-in scenarios. A finite state machine (FSM) is designed to manage vehicle control in different cut-in scenarios. For a cut-in scenario, a method to identify and quantify the possibility of cut-in of a vehicle is proposed. At the same time, a safety distance model of the cut-in vehicle is established as the basis for the state transition of the finite state machine. Taking the quantified cut-in possibility of a vehicle as a reference, the model predictive control (MPC) algorithm, which considers the constraints of driving safety and comfort, is used to realize coordinated control of the host vehicle and the cut-in vehicle. Simulink–Carsim simulation studies show that the ACC strategy for a cut-in scenario can effectively identify a cut-in vehicle and quantify the possibility of cut-in of the vehicle. Faced with a cut-in vehicle, the host vehicle using the ACC strategy can brake several seconds early and switch the following target to the cut-in vehicle. Meanwhile, the acceleration and jerk of the host vehicle changes within a reasonable range, which ensures driving safety and comfort.
Highlights
With the rapid development of computers, electronics, and sensor technology, advanced driver assistance systems (ADAS) have become an important direction for development of the automotive industry because they have great advantages in improving transportation efficiency [1] and driving safety [2]
When facing the lane-change maneuvers of a nearby car, a vehicle with an adaptive cruise control (ACC) system has to switch the following target to the cut-in vehicle and follow its trajectory
HoInstoVrdeehrictolemTarkajeeicttsoorythTartaccukti-ningvCeohnictlreoLl does not collide with host vehicle H during the lane change process, the following equation needs to be satisfied: This section uses the model predictive control (MPC) method to follow the trajectory ojwufidtaghcitnSuhgrte(-itahn)ov=vseethhvicieclhell2ei’s(ca0lcen)uda+tn(-evidnsL0tt(iah0nb0e)tλlei−p(snarhtLveieo(cHsτen(ad),0i−t)Mnh)geaP×HvCcute(-τh−bt-)iaic)nlsl0deevτdiednAhλfi+CrcolCentscaoo>ffnehtt0ryoosdlt∀isvsyttesa∈htneiccmetlecm(+iMnotPtdahdCeejl,sTiAsamCcoCem)l.apAna(e2frt.e6eId)rf tthanecceAumrcrcoeondrtedlrienislgauttsioveedth.deTisghteeaonlomcneegmtirtieucedrtesinltaahtlieotrsnaasjfehecitptyo,rdtyhiseotafcntahclceeucmluaott-idoinenlv,fetohhremiccluueltaa-fiotnefrvl1ee(nh0ti)celraeinnsdgaftle2ht(ey0l)dainisse:
Summary
With the rapid development of computers, electronics, and sensor technology, advanced driver assistance systems (ADAS) have become an important direction for development of the automotive industry because they have great advantages in improving transportation efficiency [1] and driving safety [2]. When facing the lane-change maneuvers of a nearby car, a vehicle with an ACC system has to switch the following target to the cut-in vehicle and follow its trajectory. In this process, there may be a problem of braking too late as well as excessive braking. As osohffonnweeaanrrbibnyyFvviegehuhiriccelle7e,a, ,ddwllaatti,t,hWWteheggeoo isisththeewwididtthhooff lateral distance of a nearby vehicle decreases, the cut-in possibility, Pcutin_dlat, becomes greater. When a nearby vehicle shifts laterally until it overlaps with the host vehicle body longitudinal direction and touches the red dotted line, the cut-in prob is considered to be 1, which means Pcutin _ dlat =1 when dlat (1 / 2)Wh . When a nearby vehicle shifts laterally until it overlaps with the host vehicle body longitudinal direction and touches the red dotted line in Figure 7a, the cut-in prob is considered to be 1, which means Pcutin _ dlat =1 when dlat (1 / 2)Wh . 8 of 29
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