- Original Paper
- Open Access
- Published:

# A new adaptive cruise control strategy and its stabilization effect on traffic flow

*European Transport Research Review*
**volume 10**, Article number: 49 (2018)

## Abstract

Connected and autonomous vehicle (CAV) technologies are likely to be gradually implemented over time. In this paper, an adaptive cruise control, named Smart Driver Model (SDM), is proposed to describe the autonomous vehicles flow. The stability criteria is proposed for SDM to judge the stability of homogeneous traffic flow. Numerical simulations were conducted to verify the results of the theoretical analysis. Single-lane vehicle dynamics in a traffic stream with connected and autonomous vehicles are simulated by varying model parameters. Simulation results are consistent with the results of linear stability analysis. As a result, a set of parameters is proposed to investigate the stabilization effect of the proposed model on homogeneous traffic flow considering realistic driving cycle and cut-in condition. By simulating a platoon with a lead vehicle which follows the Urban Dynamometer Driving Schedule (UDDS), we find out that the proposed model can stabilize the traffic flow with proposed parameters. The results from simulation and linear stability analysis show that SDM outperforms the IDM-ACC and the ACC proposed by Milanés and Shladover in terms of stabilization effect on homogeneous traffic flow. The simulation result shows that the SDM-equipped vehicles are able to stabilize the homogeneous traffic flow under cut-in condition.

## Introduction

Connected and autonomous vehicle (CAV) technologies have gained a lot of attention all over the world because of its potential in improving safety and congestion of the road transportation system. In particular, adaptive cruise control (ACC) is one of the most important CAV technologies that can enhance driving comfort, reduce driving errors, improve safety, increase traffic capacity and reduce fuel consumption [1].

Existing ACC methods are categorized into optimization-based ACCs, artificial intelligence technique-based ACCs and rule-based ACCs. Considering the fuel efficiency, signalized intersection, and road geometry, several optimization-based ACCs have been developed [2,3,4,5,6]. Naranjo et al. [7] proposed an ACC based on artificial intelligence techniques. Moreover, existing rule-based ACCs are based on three main headway selection policies: Constant Space-Headway (CSH), Constant Time-Headway (CTH) and Variable Time-Headway (VTH) [8]. CSH is not string stable and not suitable for ACC systems [9,10,11]. Among these three headway selection policies, the ACCs based on CTH are most closely related to normal manual car-following behavior. Moreover, Swaroop et al. [12] pointed out that the control design and stabilization challenges of the CTH-based car following criteria are significantly easier for other rule-based car-following models. As a result, in this paper, we focus on rule-based ACCs with constant time headway policies.

Several rule-based ACC methods have been proposed in the literature. Davis [13] proposed an ACC that automatically maintained a safe distance and minimized the speed difference between the following vehicle and its immediate preceding vehicle. Kesting et al. [14] proposed an ACC based on intelligent driver model (IDM) and inherited its intuitive parameters proposed by Treiber et al. [15]. The ACC proposed by Kesting et al. partially eliminates the unrealistic behavior of IDM in cut-in situations. However, since the ACC proposed by Kesting et al. inherits the instability of IDM under homogenous traffic flow condition [16]. Moreover, an ACC, which is proprietary to Nissan, was described by Shladover et al. [17]. Since this ACC is simplified representation for computation efficiency in simulation, it is not able to describe the ACC car-following behavior in the field test. Consequently, Milanés and Shladover [18] developed an ACC system to match the experimental result obtained from the production vehicle road tests. However, their research did not consider the stability in multi-car following scenarios.

By modeling ACC-equipped vehicles’ behavior, the impact of ACC on traffic flow is widely studied [10, 19, 20]. Davis showed that the traffic congestions are not formed when the ACC-equipped vehicle reaches 20% [13]. Kesting et al. reported that the traffic congestion was completely eliminated when the share of the ACC-equipped vehicle reaches 25% [14]. Jerath and Brennan reported that the highway capacity drastically increases when the percentage of the ACC-equipped vehicle approaches 100% [21]. Moreover, by simulating a mixed traffic consisting of ACC-equipped and manually driven vehicles, Jiang et al. found that the introduction of ACC-equipped vehicles would enhance the free flow stability [22]. Yuan et al. investigated the transition probability from the synchronized flow to congestion and pointed out that ACC-equipped vehicles enhance the traffic stability of synchronized flow [23].

Consequently, scholars paid a lot of attention to the stabilization effect of ACC-equipped vehicles on traffic flow. Darbha and Rajagopal [11] mathematically investigated the stability of an intelligent cruise control system with a constant time headway policy and provided a framework for designing cruise control laws. Liang and Peng [10] proposed a framework to analysis string-stability of a string with ACC-equipped vehicles and human-driven vehicles. Davis [24] investigated the effect of vehicle response time and delay on string stability of adaptive cruise control systems. Later, Davis [25] investigated the impact of mechanical response of the dynamics and string stability of a platoon of adaptive cruise control vehicles. Hu et al. [26] derived a stability criteria of a platoon of ACC-equipped vehicles with actuator lag and sensor delay. Considering different models with the technology-appropriate assumption, Talebpour and Mahmassani [16] proposed a framework to investigate the impact of the connected and autonomous vehicle on traffic flow stability. Moreover, Davis [25] investigated the impact of mechanical response on string stability of a platoon of ACC-equipped vehicles. Considering the delay, Xing et al. [27], Wang et al. [28] and Besselink and Johansson [29] proposed appropriate control strategies to enhance string stability of ACC-equipped vehicle platoon. Moreover, Li et al. [30] proposed an extended intelligent driver model and analyzed the stability against a small perturbation by use of the linear stability method for the proposed model on a single lane. They found that the traffic flow stability can be improved by increasing the proportion of the direct power cooperation of the preceding vehicle. Recently, Wen-Xing and Li-Dong [31] proposed two lemmas and one theorem as criteria to judge the stability of homogeneous autonomous vehicles flow.

Among existing studies, most of the existing rule-based adaptive cruise control strategies are derived based on the human driver models, such as Intelligent Driver Model [15], Full Velocity Difference Model [32] and Optimal Velocity Model [33]. These adaptive cruise control strategies may inherit the limitation of the human driver models, such as instability [16, 34] and unrealistic behavior [13, 15, 33]. In this paper, we propose a rule-based adaptive cruise control to improve the stability and behavior of Intelligent Driver Model [15]. The stabilization effect of the proposed adaptive cruise control on traffic flow is discussed theoretically. Moreover, in order to estimate the performance of the proposed ACC, the stabilization effect of the proposed adaptive cruise control is compared with existing models.

The rest of the paper is organized as follows: section 2 presents the proposed adaptive cruise control strategy. The linear stability analysis of the proposed model is represented in Section 3. Section 4 provides results from simulating single-lane vehicle dynamics with or without cut-in scenarios. The conclusions and limitations are presented in Section 5.

## Smart driver model

In the past decades, numbers of car-following models have been introduced to simulate manually driven vehicle, such as Multi-anticipative Model [35], Tampere Model [36], Newell Model [37], Gipps Model [38], Full Velocity Difference Model [32] and Optimal Velocity Model [33]. Based on Gipps model, Treiber et al. proposed a human driver model named Intelligent Driver Model (IDM) [15]. By capturing different congestion dynamics, IDM provides greater realism than most of the deterministic acceleration modeling frameworks [16]. The IDM is formulated as follows:

where,

\( {a}_{IDM}^n \) is the acceleration of the following vehicle based on Intelligent Driver Model (m/s^{2});

*δ* is the acceleration exponent;

*s*_{0} is the standstill distance between stopped vehicles (m);

*a*_{max} is the maximum acceleration (m/s^{2}), which is predetermined and less than the Planck acceleration;

*∆x* is the gap between the leading and the following vehicle (m);

T is the desired time gap (s);

*v*_{0} is the maximum speed (m/s);

*v*_{n} is the speed of the following vehicle (m/s);

*v*_{n − 1} is the speed of the leading vehicle (m/s);

*s*^{∗} is the desired gap (m); and.

*b* is the desired deceleration (m/s^{2}).

In recent years, car-following models have evolved to describe the behavior of vehicles with advanced cruise controls, which take advantage of the sensing and vehicle to vehicle/vehicle to infrastructure (V2*V*/V2I) communication technologies. By using constant-acceleration heuristic (CAH) as an indicator, Kesting et al. proposed a rule-based adaptive cruise control based on IDM [34] (IDM-ACC). The ACC proposed by Kesting et al. partially eliminates the unrealistic behavior of IDM in cut-in situations. However, since the ACC proposed by Kesting et al. inherits its intuitive behavioral parameters of IDM, it inherits the instability of IDM under homogenous traffic flow condition [16].

A rule-based ACC, named Smart Driver Model (SDM) is proposed to address the instability of IDM under homogenous traffic condition. In this study, a single-lane car-following scenario is considered and the lane changing behavior is ignored. In this paper, we assume that on-board sensors measure vehicle speed, gap (relative distance) and relative speed with respect to the preceding vehicle on regular time intervals [28].

The acceleration of the following vehicle equipped with SDM is determined by the following equation:

where,

\( {a}_{SDM}^n \) is the acceleration of the following vehicle that is equipped with SDM (m/s^{2});

According to Eq. 1, SDM inherits the acceleration maneuver from Intelligent Driver Model [15]. As a result, the acceleration exponent is set as 4 according to existing studies [15, 34, 39]. Therefore, the vehicles equipped with SDM tend to accelerate with \( {a}_{max}\left[1-{\left(\frac{{\mathrm{v}}_{\mathrm{n}}}{{\mathrm{v}}_0}\right)}^4\right] \) when *∆x* is large. An SDM-equipped vehicle will brake while the speed of the SDM-equipped vehicle is greater than the leading vehicle speed and *∆x* is less than the desired spacing. \( \frac{a_{max}\left[1-{\left(\frac{v_n}{v_0}\right)}^{\delta}\right]+\frac{v_n^2-{v}_{n-1}^2}{2\Delta x}}{\mathit{\exp}\left(\frac{\Delta x}{s_0+{v}_n\times T}-1\right)} \) is used to describe the deceleration maneuver. In the deceleration term, \( {a}_{max}\left[1-{\left(\frac{v_n}{v_0}\right)}^{\delta}\right] \) is used to offset the acceleration term. Moreover, considering the range policy, it ensures that the real deceleration is equal to or slight larger than the expected deceleration, which is \( \frac{{\mathrm{v}}_{\mathrm{n}}^2-{\mathrm{v}}_{\mathrm{n}-1}^2}{2\Delta \mathrm{x}} \). Moreover, since exp(∙) > 0 and \( \exp (0)=1,\frac{1}{\exp \left(\frac{\Delta \mathrm{x}}{{\mathrm{s}}_0+{\mathrm{v}}_{\mathrm{n}}\times \mathrm{T}}-1\right)} \) is applied to describe the dependence of deceleration term on the ratio between actual spacing and desired spacing. Therefore. when there is no speed difference between the leading and following vehicle, a SDM-equipped vehicle ‘s acceleration increases with the ratio of *∆x* to the desired spacing. According to the characteristics of the exponential function, the jerk of SDM-equipped vehicle, which represents the changing rate of SDM-equipped vehicle ‘s acceleration, decreases with the ratio of ∆x to the desired spacing. Consequently, the vehicle equipped with SDM can achieve smoother acceleration and deceleration which is able to stabilize the traffic flow.

Several properties of SDM are discussed as follows, considering special cases:

First, when SDM-equipped vehicle is cruising (i.e. \( {a}_{SDM}^n=0,{v}_n-{v}_{n-1}=0 \)), the speed-dependent spacing *∆x* between the preceding and the following vehicle is given by

The speed-dependent spacing *∆x* equals to the desired spacing, that is, *∆x* = *s*_{0} + *v*_{n} × *T*.

Second, when the traffic density is low (i.e. *∆x* is much larger than the desired spacing), SDM-equipped vehicles will accelerate to the maximum speed. When the leading vehicle is out of the detection range of SDM-equipped vehicle (*∆x* → ∞), \( \frac{{\mathrm{v}}_{\mathrm{n}}^2-{\mathrm{v}}_{\mathrm{n}-1}^2}{2\Delta \mathrm{x}} \) is close to 0 and \( {e}^{\left(\frac{\Delta \mathrm{x}}{{\mathrm{s}}_0+{\mathrm{v}}_{\mathrm{n}}\times \mathrm{T}}-1\right)} \) is close to infinity. As a result, the acceleration of SDM is approximately equal to the maximum acceleration, \( {a}_{SDM}^n\approx {a}_{\mathrm{max}}\times \left[1-{\left(\frac{{\mathrm{v}}_{\mathrm{n}}}{{\mathrm{v}}_0}\right)}^4\right] \). After the speed of the SDM-equipped vehicle reaches the maximum speed, acceleration of SDM is 0.

Third, when SDM-equipped vehicle is following a slower vehicle or approaching a stopped vehicle (i.e. *v*_{n} − *v*_{n − 1}> 0) with the limited spacing (*∆x* → *s*_{0} + *v*_{0} × *T*), the acceleration equation of SDM is given by

Especially, when a SDM-equipped vehicle with the maximum speed approaches a stopped vehicle (i.e. v_{n} = v_{0}, v_{n − 1} = 0), the maximum kinematic deceleration is applied to avoid a collision, as follows.

Forth, when the spacing is much smaller than the desired spacing (*∆x* ≪ *s*_{0} + *v*_{0} × *T*) and there is no significant speed differences (*v*_{n} − *v*_{n − 1} ≈ 0), the acceleration is determined as follows:

Especially, when *∆x* → 0, Eq. 5 reduces to

## Linear stability analysis

Linear stability method is widely applied to analyze the stabilization performance of car-following models [40,41,42,43,44,45,46,47]. In this section, we apply the linear stability method for the Smart Driver Model described in the last section. The general form of time-continuous car-following models is

Where,

*f* is a general nonlinear function;

\( {\ddot{x}}_n(t) \) is the acceleration;

*τ* is the time delay;

*v*_{n}(*t*) is the speed of vehicle n;

*s*_{n}(*t*) is the spacing between vehicles n and n + 1; and.

*∆v*_{n}(*t*) is the relative velocity between vehicles n and n + 1.

Moreover, the steady state of Eq. 1 is set as

Where,

*s*_{e} is the spacing of adjacent vehicles in homogenous flow;

N is the total number of vehicles in homogenous flow;

*v*_{e} is the speed of vehicles in homogenous flow; and.

\( \overline{x_n}(t) \) is the location of vehicle n at time t.

Moreover, *y*_{n}(*t*) is introduced as a small perturbation from the steady state solution of the vehicle n at time t with the linear Fourier-mode expanding:

By taking the second derivative of Eq. 9, we can obtain

By substituting Eq. 7 into Eq. 10, we have

By linearizing Eq. 11, we get the following equation

Where \( {f}_v={\left.\frac{\partial f}{\partial {v}_n}\right|}_{\left({v}_e,{s}_e,0\right)}\le 0 \), \( {f}_{\Delta v}={\left.\frac{\partial f}{\partial {\Delta v}_n}\right|}_{\left({v}_e,{s}_e,0\right)}\le 0 \), and \( {f}_s={\left.\frac{\partial f}{\partial {s}_n}\right|}_{\left({v}_e,{s}_e,0\right)}\ge 0 \).

We rewrite Eq. 12 to obtain the difference equation

By substituting \( {y}_n(t)=c{e}^{i{a}_kn+ zt} \) into Eq. 13, we have

Where c is constant, *a*_{k} = 2*πk*/*N*(*k* = 0, 1, …, *N* − 1).

By expanding *z* = *z*_{1}(*ia*_{k}) + *z*_{2}(*ia*_{k})^{2} + …, \( {e}^{\tau z}=1+\tau z+\frac{\tau^2{z}^2}{2}+\bullet \bullet \bullet, \)and inserting it into Eq. 13, then the first order and second order terms of coefficients of the expression of z are described as follows

If *z*_{2} < 0, the homogenous traffic flow is unstable. Alternatively, If *z*_{2} > 0, the homogenous traffic flow is stable. Therefore, the stability condition is described as follows

This stability condition is consistent with existing studies [30, 48].

In order to investigate the stabilization effect of the SDM model on homogeneous traffic flow, we have

By substituting Eqs. 18–20 into Eq. 17, the stable condition is derived as follows

According to Eq. 23, the phase transition curves are derived. Figure 1 shows the neutral stability curves for different time delays, where the maximum acceleration *a* = 1.4 m/s^{2}, maximum speed *v*_{0} = 30 m/s, and the standstill distance *s*_{0} = 1.5 m. The traffic flow is stable with uniform velocity steady state above the neutral stability line; conversely, the traffic is unstable and the traffic congestion emerges. From Fig. 1, one can observe that the neutral stability line moves up with the increase of time delay. The effect of the stability of traffic flow by considering the time delay can be further illustrated in Fig. 2, where the average speed *v*_{e} = 4 m/s, maximum speed *v*_{0} = 30 m/s, and the standstill distance *s*_{0} = 1.5 m. Same as in the study conducted by Van Arem et al. [49], the maximum comfortable acceleration is 2 m/s^{2}. From Fig. 2, one can see that by increasing the maximum acceleration *a*, the neutral stability curve changes down, which means that traffic flow will become more and more stable if the maximum acceleration of SDM-equipped vehicle increases, i.e., the traffic stability can be improved by raising the maximum acceleration. However, by increasing the maximum acceleration may make driving less comfortable. The improvement in traffic stability by decreasing the time delay also can be found here.

According to Eq. 19, the stability of SDM is compared with IDM-ACC and the ACC proposed by Milanés and Shladover [18] by using the typical parameters from literature [18, 34]. Since IDM-ACC only eliminates the unrealistic behavior of IDM in cut-in situations, the linear stability of IDM-ACC is the same as the linear stability of IDM. As shown in Fig. 3, the ACC proposed by Milanés and Shladover is unstable with the proposed parameters. Same as the result in [16], the platoon of vehicles equipped with IDM-ACC is stable for speeds below 3.5 m/s and unstable for speed above 3.5 m/s. Moreover, the stability curve of SDM is above 0. When the speed is larger than 3.5 m/s, SDM outperforms IDM-ACC in terms of stability.

## Numerical simulations

### Platoon without cut-in

To validate the results of our theoretical stability analysis, a platoon with 100 vehicles are simulated on a one-lane highway (no lane-changing) with an infinite length. Moreover, the time delay of ACC-equipped vehicle are ignored to investigate the stabilization performance of the proposed model under ideal conditions. Two cases are conducted in this section.

#### Case 1

A simple case is applied to investigate the impacts of desired time gap and maximum acceleration on the stabilization performance of the proposed model. Initially, the speed *v*_{initial}=10 m/s, stand still distance *s*_{0}= 1.5 m and the spacing *s*_{initial}=*s*_{0} + *v*_{initial} × *T*. The initial steady state will be broken when the leading vehicle brakes urgently from 10 to 4 m/s during the time period 10 s ≤ *t* ≤ 13 s, followed by the following vehicle moving according to Eq. (4). As shown in Fig. 4(a), the traffic flow is unstable when the desired time gap is less than 1.6, where the maximum acceleration is 1.4 m/s^{2}. Moreover, while the desired time gap is 1.6, the traffic flow is unstable when the maximum acceleration is 0.8 m/s^{2}. The results are consistent with the results derived in last section.

#### Case 2

In order to investigate the stabilization effect of the proposed model considering the realistic driving cycle, we assume the lead vehicle of platoon follows the Urban Dynamometer Driving Schedule (UDDS), as shown in Fig. 5. Moreover, the initial spacing and time headway are the desired spacing and desired time gap, respectively. According to the result of case 1, we set the maximum acceleration a = 1.4 m/s^{2}, desired time gap T = 1.6 s, maximum speed v_{0} = 30 m/s, and the standstill distance s_{0} = 1.5 m. The parameters from case 1 is similar to the parameters of IDM-ACC proposed by Kesting et al. [34]. Moreover, the ACC proposed by Milanés and Shladover [18] is also simulated to compare with the IDM-ACC and SDM. In order to avoid the instability of the ACC proposed by Milanés and Shladover [18], we have *k*_{1} = 0.49 and *k*_{2} = 0.07, where the stability calculated based on Eq. 19 is 0.02. Therefore, the parameters from case 1 is used in the simulation for both IDM-ACC and SDM. As shown in Fig. 6, the acceleration variance is significantly decreasing for the SDM-equipped vehicle towards the end of the platoon. With before mentioned parameters, the proposed model can stabilize the homogeneous traffic flow considering a realistic driving cycle. Moreover, SDM outperforms the IDM-ACC and Milanés and Shladover Model in terms of stabilization effect on homogeneous traffic flow.

### Platoon with cut-in

Based on the simulation conducted by [50], a platoon with 16 vehicles, when a cut-in occurs between the fourth and fifth positions, is simulated to investigate the stability of SDM under cut-in condition. The cut-in rules and parameters are based on the study conducted by Davis [51]. The simulation starts with the 16 SDM-equipped vehicles. The leading vehicle is manually driven and its speed profile is generated using real data from experimental tests. The other vehicles follow the leading vehicle. One can appreciate how the speed changes are not amplified upstream. Then a vehicle cuts in between the fourth and fifth vehicles. For the sake of clarity in the figure, the speeds of the first four vehicles have been removed from the plots of the results for the rest of the simulation. From second 340, the cut-in vehicle depicted in the graph with the red solid lane splits the string in two. This vehicle is computer generated and it is assumed that it is manually driven with small speed oscillations around 25 m/s. As shown in Fig. 7, the oscillations from the cut-in vehicle are reduced by the 5th to the 16th vehicles. With the proposed parameters, the proposed model can stabilize the homogeneous traffic flow under cut-in condition.

## Conclusion and future work

In this paper, an adaptive cruise control, named Smart Driver Model (SDM), is proposed to describe the autonomous vehicles flow. Meanwhile, the stability criteria is proposed for SDM to keep the homogeneous traffic flow stable. Numerical simulations were conducted to verify the results of the theoretical analysis. We find that simulation results are consistent with the results of linear stability analysis. Considering a platoon without cut-in, the simulation result shows that the proposed model can stabilize the homogeneous traffic flow considering a realistic driving cycle. Moreover, the results from simulation and linear stability analysis show that SDM outperforms the IDM-ACC and the ACC proposed by Milanés and Shladover in terms of stabilization effect on homogeneous traffic flow. Considering a platoon with cut-in, a platoon with 16 vehicles, when a cut-in occurs between the fourth and fifth positions, is simulated to investigate the stability of SDM under cut-in condition. The simulation result shows that the SDM-equipped vehicles are able to stabilize the homogeneous traffic flow under cut-in condition. In the future, considering the information from multiple preceding vehicles and the surrounding traffic, SDM can be extended to energy-efficient control strategies and personalized information systems.

The present paper has the following limitations. First, the stability analysis is based on the theoretical model and simulation. Second, the lane-changing behavior is ignored to investigate the stability of the proposed model. Third, heterogeneous traffic conditions with ACC-equipped vehicles and human-driven vehicles is not considered in this paper. The fourth limitation comes from the same model parameter setting within a vehicle string in the simulation experiments. The impacts of different driver characteristics, such as reaction time, acceleration capabilities, and desired time gaps, within a string can be found by a sensitive analysis in subsequent simulations. In the future, the data collected from SDM-equipped vehicles should be used to validate the theoretical results. The proposed model will be implemented in an advanced and sophisticated traffic simulation model to discover the traffic impact of ACC vehicles on heterogeneous traffic flow. Moreover, specific lane-change maneuver needs to be designed for SDM in order to improve the stabilization effect of autonomous vehicles on the road transportation system.

## References

- 1.
Xiao L, Gao F (2010) A comprehensive review of the development of adaptive cruise control systems. Veh Syst Dyn 48:1167–1192. https://doi.org/10.1080/00423110903365910.

- 2.
Yang H, Rakha H, Ala MV (2017) Eco-cooperative adaptive cruise control at signalized intersections considering queue effects. IEEE Trans Intell Transp Syst 18:1575–1585.

- 3.
Vajedi M, Azad NL (2016) Ecological Adaptive Cruise Controller for Hybrid Electric Vehicles Using Nonlinear Model Predictive Control. IEEE Trans Intell Transp Syst 17:113–122. https://doi.org/10.1109/TITS.2015.2462843.

- 4.
Zhou F, Li X, Ma J (2017) Parsimonious shooting heuristic for trajectory design of connected automated traffic part I: theoretical analysis with generalized time geography. Transp Res Part B Methodol 95:394–420. https://doi.org/10.1016/j.trb.2016.05.007.

- 5.
Ahn K, Rakha HA, Park S (2013) Ecodrive application algorithmic development and preliminary testing. Transp Res Rec J Transp Res Board 2341:1–11. https://doi.org/10.3141/2341-01.

- 6.
Liang C, Peng H (1999) Optimal adaptive cruise control with guaranteed string stability. Veh Syst Dyn 31:313–330.

- 7.
Naranjo JE, Gonzalez C, Garcia R, dePedro T (2006) ACC+Stop&Go Maneuvers With Throttle and Brake Fuzzy Control. IEEE Trans Intell Transp Syst 7:213–225. https://doi.org/10.1109/TITS.2006.874723.

- 8.
Ntousakis IA, Nikolos IK, Papageorgiou M (2015) On microscopic modelling of adaptive cruise control systems. Transp Res Procedia 6:111–127. https://doi.org/10.1016/j.trpro.2015.03.010.

- 9.
Rajamani R (2012) Vehicle dynamics and control. Springer science, US, Boston.

- 10.
Liang C, Peng H (2000) String stability analysis of adaptive cruise controlled Vehciles. JSME Int J Ser C Mech Syst Mach Elem Manuf 13:671–677.

- 11.
Darbha S, Rajagopal KR (1999) Intelligent cruise control systems and traffic flow stability. Transp Res Part C 7:329–352.

- 12.
Swaroop D, Hedrick JK, Chien CC, Ioannou P (1994) A Comparision of spacing and headway control Laws for automatically controlled vehicles 1 ’, Vehicle System Dynamics. Veh Syst Dyn 23:597–625. https://doi.org/10.1080/00423119408969077.

- 13.
Davis LC (2004) Effect of adaptive cruise control systems on traffic flow. Phys Rev E - Stat Nonlinear, Soft Matter Phys 69:1–8. https://doi.org/10.1103/PhysRevE.69.066110.

- 14.
Kesting A, Treiber M, Schönhof M, Helbing D (2008) Adaptive cruise control design for active congestion avoidance. Transp Res Part C Emerg Technol 16:668–683. https://doi.org/10.1016/j.trc.2007.12.004.

- 15.
Treiber M, Hennecke A, Helbing D (2000) Congested traffic states in empirical observations and microscopic simulations. Phys Rev 62:1805–1824. https://doi.org/10.1103/PhysRevE.62.1805.

- 16.
Talebpour A, Mahmassani HS (2016) Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transp Res Part C Emerg Technol 71:143–163. https://doi.org/10.1016/j.trc.2016.07.007.

- 17.
Shladover SE, Su D, Lu X-Y (2012) Impacts of cooperative adaptive cruise control on freeway traffic flow. Transp Res Rec J Transp Res Board 2324:63–70. https://doi.org/10.3141/2324-08.

- 18.
Milanés V, Shladover SE (2014) Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data. Transp Res Part C Emerg Technol 48:285–300. https://doi.org/10.1016/j.trc.2014.09.001.

- 19.
Kikuchi S, Uno N, Tanaka M (2003) Impacts of shorter perception-reaction time of adapted cruise controlled vehicles on traffic flow and safety. J Transp Eng 129:146–154.

- 20.
Fancher P, Peng H, Bareket Z et al (2002) Evaluating the influences of adaptive cruise control systems on the longitudinal dynamics of strings of highway vehicles. Veh Syst Dyn 37:125–136. https://doi.org/10.1080/00423114.2002.11666226.

- 21.
Jerath K, Brennan SN (2012) Analytical prediction of self-organized traffic jams as a function of increasing ACC penetration. IEEE Trans Intell Transp Syst 13:1782–1791. https://doi.org/10.1109/TITS.2012.2217742.

- 22.
Jiang R, Hu M, Jia B et al (2007) Phase transition in a mixture of adaptive cruise control vehicles. Eur Phys J B 206:197–206. https://doi.org/10.1140/epjb/e2007-00215-3.

- 23.
Yuan Y, Jiang R, Hu M et al (2009) Traffic flow characteristics in a mixed traffic system consisting of ACC vehicles and manual vehicles : a hybrid modelling approach. Phys A Stat Mech its Appl 388:2483–2491. https://doi.org/10.1016/j.physa.2009.02.033.

- 24.
Davis LC (2012) Stability of adaptive cruise control systems taking account of vehicle response time and delay. Phys Lett A 376:2658–2662. https://doi.org/10.1016/j.physleta.2012.07.019.

- 25.
Davis LC (2013) The effects of mechanical response on the dynamics and string stability of a platoon of adaptive cruise control vehicles. Phys A Stat Mech its Appl 392:3798–3805. https://doi.org/10.1016/j.physa.2013.04.023.

- 26.
Hu SG, Wen HY, Xu L, Fu H (2017) Stability of platoon of adaptive cruise control vehicles with time delay. Transp Lett 7867:1–10. https://doi.org/10.1080/19427867.2017.1407488.

- 27.
Xing H, Ploeg J, Nijmeijer H (2016) Pade Approximation of Delays in Cooperative ACC Based on String Stability Requirements. IEEE Trans Intell Transp Syst 1:277–286.

- 28.
Wang M, Hoogendoorn SP, Daamen W et al (2018) Delay-compensating strategy to enhance string stability of autonomous vehicle platoons. Transp B Transp Dyn 6:211–229. https://doi.org/10.1080/21680566.2016.1266973.

- 29.
Besselink B, Johansson KH (2017) String stability and a delay-based spacing policy for vehicle platoons subject to disturbances. IEEE Trans Automat Contr 62:1–15. https://doi.org/10.1109/TAC.2017.2682421.

- 30.
Li Z, Li W, Xu S, Qian Y (2015) Stability analysis of an extended intelligent driver model and its simulations under open boundary condition. Phys A Stat Mech its Appl 419:526–536. https://doi.org/10.1016/j.physa.2014.10.063.

- 31.
Wen-Xing Z, Li-Dong Z (2017) A new car-following model for autonomous vehicles flow with mean expected velocity field. Phys A Stat Mech its Appl 492:2154–2165. https://doi.org/10.1016/j.physa.2017.11.133.

- 32.
Jiang R, Wu Q, Zhu Z (2001) Full velocity difference model for a car-following theory. Phys Rev E - Stat Nonlinear, Soft Matter Phys 64:017101. https://doi.org/10.1103/PhysRevE.64.017101.

- 33.
Bando M, Hasebe K, Nakayama A et al (1995) Dynamical model of traffic congestion and numerical simulation. Phys Rev E 51:1035.

- 34.
Kesting A, Treiber M, Helbing D (2010) Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity. Philos Trans A Math Phys Eng Sci 368:4585–4605. https://doi.org/10.1098/rsta.2010.0084.

- 35.
Lenz H, Wagner CK, Sollacher R (1999) Multi-anticipative car-following model. Eur Phys J B 335:331–335.

- 36.
Tampere CMJ (2004) Human-Kinetic Multiclass Traffic Flow Theory and Modelling With Application to Advanced Driver Assistance Systems in Congestion.

- 37.
Newell GF (1961) Nonlinear effects in the dynamics of Car following. Oper Res 9:209–229. https://doi.org/10.1287/opre.9.2.209.

- 38.
Gipps PG (1981) A behavioural car-following model for computer simulation. Transp Res Part B 15:105–111. https://doi.org/10.1016/0191-2615(81)90037-0.

- 39.
Wang Z, Chen XM, Ouyang Y, Li M (2015) Emission mitigation via longitudinal control of intelligent vehicles in a congested platoon. Comput Civ Infrastruct Eng 30:490–506. https://doi.org/10.1111/mice.12130.

- 40.
Ge HX, Dai SQ, Dong LY, Xue Y (2004) Stabilization effect of traffic flow in an extended car-following model based on an intelligent transportation system application. Phys Rev E - Stat Nonlinear, Soft Matter Phys 70:1–6. https://doi.org/10.1103/PhysRevE.70.066134.

- 41.
Pei X, Pan Y, Wang H et al (2016) Empirical evidence and stability analysis of the linear car-following model with gamma-distributed memory effect. Phys A Stat Mech its Appl 449:311–323. https://doi.org/10.1016/j.physa.2015.12.104.

- 42.
Jia Y, Du Y, Wu J (2014, 2014) Stability analysis of a Car-following model on two lanes. Math Probl Eng:1–9. https://doi.org/10.1103/PhysRevE.72.066124.

- 43.
Wilson RE, Ward JA (2011) Car-following models: fifty years of linear stability analysis - a mathematical perspective. Transp Plan Technol 34:3–18. https://doi.org/10.1080/03081060.2011.530826.

- 44.
Tang TQ, Huang HJ, Gao ZY (2005) Stability of the car-following model on two lanes. Phys Rev E - Stat Nonlinear, Soft Matter Phys 72:1–7. https://doi.org/10.1103/PhysRevE.72.066124.

- 45.
Monteil J, Billot R, Sau J, El Faouzi NE (2014) Linear and weakly nonlinear stability analyses of cooperative car-following models. IEEE Trans Intell Transp Syst 15:2001–2013. https://doi.org/10.1109/TITS.2014.2308435.

- 46.
Ge H-X, Meng X-P, Zhu K-Q, Rong-Jun C (2014) The stability analysis for an extended Car following model based on control theory. Chin Phys Lett 31:080505. https://doi.org/10.1088/0256-307X/31/8/080505.

- 47.
Li Y, Zhu H, Cen M et al (2013) On the stability analysis of microscopic traffic car-following model: a case study. Nonlinear Dyn 74:335–343. https://doi.org/10.1007/s11071-013-0973-x.

- 48.
Chen X, Li R, Xie W, Shi Q (2009) Stabilization of traffic flow based on multi-anticipative intelligent driver model. the 12th International IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, St. Louis, pp 72–77.

- 49.
Van Arem B, Van Driel CJG, Visser R (2006) The impact of cooperative adaptive cruise control on traffic-flow characteristics. IEEE Trans Intell Transp Syst 7:429–436. https://doi.org/10.1109/TITS.2006.884615.

- 50.
Milanés V, Shladover SE (2016) Handling cut-in vehicles in strings of cooperative adaptive cruise control vehicles. J Intell Transp Syst Technol Planning, Oper 20:178–191. https://doi.org/10.1080/15472450.2015.1016023.

- 51.
Davis LC (2007) Effect of adaptive cruise control systems on mixed traffic flow near an on-ramp. Phys A Stat Mech its Appl 379:274–290. https://doi.org/10.1016/j.physa.2006.12.017.

- 52.
U.S Environmental Protection Agency Dynamometer Drive Schedules. https://www.epa.gov/vehicle-and-fuel-emissions-testing/dynamometer-drive-schedules. Accessed 28 Jul 2017.

## Author information

### Affiliations

### Contributions

CL developed the ACC and drafted the paper. AA participated in the design of the study. Both authors read and approved the final manuscript.

### Corresponding author

Correspondence to Chaoru Lu.

## Ethics declarations

### Competing interests

The authors declare that they have no competing interests.

### Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Rights and permissions

**Open Access** This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

## About this article

#### Received

#### Accepted

#### Published

#### DOI

### Keywords

- Autonomous vehicle
- Adaptive cruise control
- Traffic flow stability