Nonlinear MPC for Emission EfficientCooperative Adaptive Cruise Control

Roman Schmied ∗ Harald Waschl ∗ Rien Quirynen ∗∗

Moritz Diehl ∗∗ Luigi del Re ∗

∗ Institute for Design and Control of Mechatronical Systems, JohannesKepler University Linz, Austria (e-mail: {roman.schmied,

harald.waschl, luigi.delre}@jku.at)∗∗Department of Electrical Engineering, KU Leuven University,

Belgium & Department IMTEK, University of Freiburg, Germany

Abstract: Advanced driver assistant systems (ADAS) are primarily introduced to increasesafety in every day traffic situations. Adaptive cruise control (ACC) systems represent animportant example for such ADAS. The worldwide increasing traffic volume and the demandfor the reduction of overall emissions call for the development of ADAS which concern notonly safety but also the reduction of vehicle emissions and fuel consumption. In this work acooperative adaptive cruise control (CACC) approach is introduced which focuses on thesegoals. A scenario with two consecutive driving vehicles and infrastructure-to-vehicle (I2V)communication is considered. The rear vehicle’s longitudinal dynamics are controlled by anonlinear model predictive control (NMPC) scheme with the target of emission and fuelefficient driving. The prospective velocity of the preceding vehicle is estimated by a predictionmodel based on the measured inter-vehicle distance and the I2V communication to enablean anticipatory driving behavior for the controlled vehicle. The results of hardware-in-the-loop(HIL) experiments on a dynamic engine test bench are presented and show a significant reductionof vehicle emissions and fuel consumption.

Keywords: Model Predictive Control, Intelligent Cruise Control, Automotive Emissions.

1. INTRODUCTION

Advanced driver assistant systems (ADAS) represent sin-gle parts of the puzzle on the way to autonomous driving.Supporting the driver in various every day traffic situationsthe interaction of ADAS to the vehicle varies from just giv-ing advice or warnings, like the lane keeping assistant, upto taking over the whole vehicle control, as it is the case inparking assistance. Adaptive cruise control (ACC) systemsrepresent a mixture, controlling longitudinal motion of thevehicle for general operation but leaving steering as wellas emergency actions to the driver.

Such systems are nowadays available in production ve-hicles and implemented such that the vehicle automati-cally adapts its velocity according to a predecessor (if itsvelocity is lower than a setpoint) using a specific time-lag, see Xiao and Gao (2010) for a detailed review of thedevelopment of ACC systems. Several extensions and en-hancements of ACC have been of high interest in academicand industrial research for the last years, mostly knownunder the name of cooperative adaptive cruise control(CACC). Thereby, communication either between vehicles(V2V) or between surrounding infrastructure and vehicles(I2V/V2I) is introduced to pursue different objectives.

Other approaches, Asadi and Vahidi (2011), Jones et al.(2014) aim to optimize trip duration and fuel consumptionby introducing I2V communication between upcomingtraffic lights and the controlled vehicle. Assuming that

the driving route and the scheduling of traffic lightsare known, traveling time and fuel consumption can bereduced using a receding horizon optimization strategy.However, it should be mentioned that within these works,influences of other traffic participants are neglected.

In the car following scenario considered in this paper thetarget of the controlled ego-vehicle is to reduce engineemissions and fuel consumption compared to its prede-cessor under the assumption of identical vehicles. Thisis achieved by introducing an admissible corridor for theinter-vehicle distance instead of using a specific time-gapvalue as it is the case in many ACC and CACC approaches.Within this corridor the ego-vehicle is free to move justpursuing the objective of increasing emission and fuelefficiency. Previous work using similar approaches for theinter-vehicle distance restriction focuses on fuel efficiencyonly and assumes that the velocity profile of the predeces-sor is known a priori, performing an offline optimization(Lang et al. (2013)) or a receding horizon strategy (Stangerand del Re (2013)).

However, typically the prospective behavior of other traf-fic participants is not known which makes an estima-tion of this external disturbance necessary. Some recentpublications (Schmied et al. (2015b), Lang et al. (2014))present data based heuristic prediction model approachesfor the preceding driver’s velocity and its effect on fuelefficiency. In Schmied et al. (2015c) a model predictivecontrol (MPC) strategy is implemented and evaluated

Preprints, 5th IFAC Conference on Nonlinear Model Predictive ControlSeptember 17-20, 2015. Seville, Spain

Copyright © 2015 IFAC 160

ego-vehicle

radar

predecessor

Fig. 1. CACC scenario considering I2V communicationand measurement of the preceding vehicle.

experimentally with a test vehicle confirming results ofprevious simulations also on a real road. The approach inMoser et al. (2015a) uses stochastic modeling and MPCto account for prediction uncertainties within the controltask. These approaches show high potential and possiblefurther benefits in the reduction of fuel consumption.

In this paper a CACC scenario as illustrated in Fig. 1is considered where the rear vehicle is referred to as thecontrolled ego-vehicle measuring relative distance and ve-locity of its predecessor with a radar sensor. Further, I2Vcommunication is assumed to be available providing posi-tion and scheduling of upcoming traffic lights. A nonlinearmodel predictive control (NPMC) approach is applied tooptimally control longitudinal dynamics of the ego-vehicleconcerning engine emissions and fuel consumption.

Based on the information available from radar and I2Vcommunication a prediction model is established to es-timate the prospective velocity profile of the precedingvehicle. This enables an anticipatory driving behavior forthe ego-vehicle which has been identified by previous re-search as crucial aspect for further increasing fuel efficiency. Prediction uncertainties are addressed indirectly withinthe cost function of the NMPC formulation. Simulationresults are validated by performing hardware-in-the-loop(HIL) experiments on a dynamic engine test bench mea-suring engine emissions and fuel consumption precisely.The measurement results show a significant reduction ofemissions and fuel consumption.

The paper first gives a general description of the problemincluding vehicle modeling and a background on engineemissions. Thereafter the structure and implementationof the NMPC as well as a disturbance prediction modelto estimate the prospective velocity of the predecessoris described. Subsequently the paper presents the HILmeasurement results and concludes with a summary andoutlook.

2. GENERAL PROBLEM DESCRIPTION

Many ACC and CACC approaches rely on a specific valuefor the inter-vehicle distance ∆x between the controlledego-vehicle and its predecessor. Several spacing policies arediscussed in literature, see e.g. Brackstone and McDonald(1999) for a historical review of car-following strategies.A widespread method is the implementation of a constanttime headway policy for the desired inter-vehicle distance∆xdes given by

∆xdes = ∆x0 + thv (1)

where ∆x0 denotes the desired distance at vehicle stand-still, v the vehicle’s velocity and th the constant timeheadway. However, all these approaches do not providea degree of freedom to enable fuel and emission efficient

ego-vehicle predecessor

ΔxminΔxmax

dcor

Fig. 2. Spacing policy with distance corridor for controlledvehicle

control since the desired inter-vehicle distance and hencethe desired velocity are fixed.

Stanger and del Re (2013) addresses this drawback byintroducing a distance corridor for the controlled ego-vehicle with boundaries ∆xmin and ∆xmax in which thevehicle is free to move. Fig. 2 depicts this strategy forthe spacing policy. For the boundaries a constant timeheadway policy according to (1) is chosen

∆xmin = ∆x0 + thv (2a)

∆xmax = ∆xmin + dcor (2b)

with a constant length dcor for the corridor.

Hence, this spacing policy introduces a degree of freedomthat allows the introduction of a control strategy for theego-vehicle targeting reduction of fuel consumption andemissions compared to the predecessor.

2.1 Vehicle Modeling

The vehicle model is based on real world measurementsperformed with a production standard vehicle BMW 320with a Diesel engine and an 8-gear automatic gear box.The longitudinal vehicle dynamics for the considered car-following scenario can be approximated by a linear modelin the form

∆x = vp − v (3a)

v = a (3b)

a =1

τv(u− a) (3c)

where x, v, a denote the position, velocity and accelerationof the vehicle, respectively, and u the input signal (gasand break pedal) normalized to the maximum acceleration.The velocity vp of the predecessor is modeled as externaldisturbance. Fig. 3 shows the modeled and measuredvehicle dynamics in form of a step response from inputsignal to acceleration to validate the linear approximation(3). Note that for illustration purposes all quantities arenormalized to their maximum.

2.2 Diesel Engine Emissions

The control target for the CACC approach presented inthis paper, namely reduction of emissions and fuel con-sumption, requires a basic understanding of the influenceof the input signal (gas pedal) in this context. As for Dieselengines NOx and particulate matter (PM) emissions aremost substantial, they are considered in this work. Thereduction of emissions traditionally is a topic in the fieldof engine injection or airpath control, see e.g. Ferreauet al. (2007), Ortner and del Re (2007), Waschl et al.

2015 IFAC NMPCSeptember 17-20, 2015. Seville, Spain

161

0 1 2 3 4 50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

time[s]

no

rma

l. g

as p

ed

al, a

cce

l. [

]

gas pedal

measured accel.

modeled accel.

Fig. 3. Normed representation of step response from inputsignal to vehicle acceleration

(2012). Other works (Schmied et al. (2015a)) examinethe influence of the injection system on engine emissionsand represent the basis for the background on emissionscovered in this section.

Different maneuvers for the gas pedal at constant en-gine speed were applied on a dynamic engine test benchequipped with a standard production 2l 4 cylinder Dieselengine which is equal to the one of the test-vehicle men-tioned above. NOx and PM emissions are captured with aHoriba 7100 and an AVL Opacimeter, respectively. Mea-surements reveal that both NOx and PM emissions showundesired peak values and an overshooting behavior fortransient pedal maneuvers. Further, considering quasi sta-tionary pedal profiles a disproportionate increase in NOxemissions could be observed.

These measurement results reveal that a reduction ofengine emissions can be achieved by keeping the pedalinput low and by avoiding fast transients. For the vehiclemodel as presented in (3) this means that changes in theinput u and acceleration a should be kept small.

3. NONLINEAR MODEL PREDICTIVE CONTROL

Generally, anticipatory driving behavior means that basedon the observation of different environmental influencesthe velocity v of the vehicle is adapted at a prematurestage. The CACC approach considered in this paper ac-counts for those influences by measuring the inter-vehicledistance ∆x and velocity vp of the preceding vehicle witha radar sensor and by assuming that infrastructure-to-vehicle (I2V) communication is available for traffic lightsproviding their position xTL and signal TL (red or green).This enables an estimation of the predecessor’s prospectivevelocity vp by introducing a prediction model. A nonlinearmodel predictive control (NMPC) strategy is introduced toachieve the objectives of emission and fuel efficient driving.Fig. 4 shows a block diagram of the control structure wherethe predecessor’s velocity is treated as a disturbance.

To ensure safety of the controlled ego-vehicle the mini-mum inter-vehicle distance ∆xmin in (2) must be keptstrictly. Previous work using MPC strategies (Schmiedet al. (2015c), Stanger and del Re (2013)) consideredthis restriction as constraint in the optimization problem.Near the minimum distance false estimation or unexpected

ego-vehicle predecessor

ΔxminΔxmax

b(Δx)

Fig. 5. Function b(∆x) arising from constraints on theinter-vehicle distance ∆x

behavior of the predecessor can easily lead to a constraintviolation and hence to a threat of safety. The approachpresented in Moser et al. (2015a) counteracts this issueby investigating a stochastic MPC explicitly consideringestimation uncertainty. Exceeding the maximum distance∆xmax is generally not crucial but should be respected tomaintain traffic flow and capacity. In this paper the re-strictions for the distance corridor are considered directlyin the cost function J of the optimal control problem usinga barrier function for the minimum distance and a penaltyfunction for the maximum distance

b(∆x) =c1

∆x−∆xmin+ e

∆x−∆xmaxc2 . (4)

Hence, robustness against false estimation of the prede-cessor’s velocity is provided and can be adjusted by theconstants c1 and c2. Fig. 5 illustrates b(∆x) arising fromconstraints on the inter-vehicle distance (4). To ensure fea-sibility of the NMPC, the measured inter-vehicle distance∆x is saturated to ∆xmin + ε with ε being a very smallconstant.

3.1 Optimal Control Formulation

Based on the results of section 2 and the distance policy (4)the following discretized optimal control problem (OCP)formulation can be obtained for receding horizon controlusing sample time Ts and horizon length N in the form

minuk+i|k

N∑i=0

η1a2k+i|k + η2u

2k+i|k + η3b

2k+i|k(∆x) (5a)

s.t. zk+1|k = Azk|k + buk|k0 ≤ vk+i|k ≤ vmax

amin ≤ ak+i|k ≤ amax i = 0 . . . N

umin ≤ uk+i|k ≤ umax .

(5b)

The notation (·)k+i|k indicates the model based predictedquantities across the control horizon N at every sam-pling instant k. Note that the OCP objective (5a) is ofnonlinear least squares type because of (4). Constraintsto velocity vk and acceleration ak are indicated by themaximum torque and breaking power of the vehicle. Thelinear state equations in (5b) do not only include thevehicle model (3). For implementation purposes, to ac-count for prospective values vp,k+i|k of the measured dis-turbance vp,k, they are considered in the state vector

zk = [∆xk vp0,k vp1,k . . . vpN ,k vk ak]T

, too. Hence, thelinear dynamics can be formulated as

2015 IFAC NMPCSeptember 17-20, 2015. Seville, Spain

162

disturbanceprediction NMPC vehicle

radar

I2VTL, xTL vp u x, v, a

∆x, vp

Fig. 4. Block diagram of control structure.

zk+1 =

1 Ts 0 . . . 0 −Ts 00 0 1 0 . . . 0 0...

. . ....

...0 . . . 1 0 00 . . . 1 0 00 . . . 0 1 Ts

0 . . . 1 0 1− Tsτv

zk

+

[0 . . . 0

Tsτv

]Tuk

(6)

The ACADO Toolkit (Houska et al. (2011), Quirynenet al. (2013)) allows one to automatically generate efficientand real-time capable solvers based on the real-time iter-ation (RTI) scheme for NMPC. The code generation toolwill be employed from Matlab/Simulink to solve (5) inreal-time using a sampling time of Ts = 0.2s.

3.2 Disturbance Prediction

In the CACC field of application the prediction of thepredecessor’s velocity vp offers the possibility of anticipa-tory driving of the controlled ego-vehicle. Whereas somepublications assume full knowledge of the predecessor’sprospective velocity profile, Lang et al. (2014) investigatesthe influence of imperfect prediction approaches in theform of nonlinear ARX models on fuel efficiency of theego-vehicle. In Moser et al. (2015b) a stochastic predictionstrategy using Bayesian networks is presented. Therebythe identification is performed with real traffic measure-ments including traffic light signals.

Those measurement data constitute the basis for theprediction approach introduced in this paper, too. Thedistance to an upcoming traffic light is given by dTL =xTL−x with xTL and x denoting the position of the nearestupcoming traffic light and the position of the vehicle,respectively . It is assumed that I2V communication islimited to a maximum distance of dTLmax

= 200m. Fig. 6depicts a measured velocity profile and the correspondingdistance dTL and signal TL (red or green) of an upcomingtraffic light.

The time delay τd between TL switching to green anda vehicle accelerating is dependent on the distance dTLwhere the vehicle comes to a standstill because of a redtraffic light signal. Fig. 7 illustrates measured values of thetime delay τd against distance to traffic light dTL. It can beobserved that those measuring points can be approximatedreasonably well by an affine function

0 50 100 150 200 250 3000

5

10

15

20

velo

city

[m/s

]

time [s]0 50 100 150 200 250 300

0

50

100

150

200

dist

ance

to T

L [m

]

Fig. 6. Velocity profile vp, distance dTL and signal TLof traffic light (red or green) used for disturbanceprediction

0 10 20 30 40 50 60 700

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

distance to TL at standstill [m]

tim

e d

ela

y τ

d [

s]

measurement

approximation

Fig. 7. Measured time delay τd as a function of the distanceto the upcoming traffic light dTL

τd = p0 + p1dTL (7)

with constant p0, p1.

Regarding the acceleration process of the vehicle after TLswitches to green, denoted here with TL the velocity vaccelcan be approximated using a first order transfer functionwhere the gain is chosen according to the actual speedlimit vlim. At sampling instant k the remaining standstillinstants ζ1,k can be defined using the time delay (7) and

the traffic light switching instant TL

ζ1,k =

(TL+

τdTs− k)σTL+ τd

Ts−k . (8)

Here, σj denotes the Heaviside function which is used toaccount for the time delay τd. The velocity prediction for

2015 IFAC NMPCSeptember 17-20, 2015. Seville, Spain

163

0 100 200 300 400 5000

5

10

15

20

ve

locity [

m/s

]

time [s]0 100 200 300 400 500

0

50

100

150

200

dis

tan

ce

to

TL

[m

]

0 100 200 300 400 500 6000

5

10

15

20

time [s]

ve

locity [

m/s

]

measurement� prediction

Fig. 8. Validation data and comparison of measured andpredicted velocity profile vk+i|k every 2 seconds

an acceleration maneuver due to a traffic light switchingto green can therefore be described as

va,k+i|k = (vlim − vk)

(1− e−

(i−ζ1,k)Ts

τa

)σi−ζ1,k (9)

i = 0 . . . N .

Concerning the braking maneuver before an upcoming redtraffic light it is supposed that the vehicle keeps the actualvelocity vk until a specific distance dTL,b to the traffic light.Generally, this distance is dependent on the traffic densitybut within this work assumed as constant. Equivalent to(8) at each sampling instant k a remaining delay

ζ2,k =dTL,k − dTL,b

vp,kTsσdTL,k−dTL,b (10)

can be introduced. The measurements in Fig. 6 revealthat after approaching dTL,b the vehicle brakes with nearlyconstant deceleration. The overall braking maneuver dueto a red traffic light can therefore be described by

vb,k+i|k = vp,k + ap,k(i− ζ2,k)Tsσi−ζ2,k (11)

i = 0 . . . N .

Using (9) and (11) the predicted velocity profile of thepreceding vehicle can be formulated as

vp,k+i|k =

{va,k+i|k TLk+i = 1, i = 0 . . . N

vb,k+i|k TLk+i = 0, i = 0 . . . N .(12)

Fig. 8 depicts a comparison between measured and pre-dicted velocity profiles where the validation data are shownin detail in the upper subplot. It can be observed that thedisturbance prediction model (12) represents a very goodapproximation to the real measured data which is mainlydue to the fact that traffic light signals are considered viaI2V communication.

4. EXPERIMENTAL RESULTS

This section presents results obtained from hardware-in-the-loop (HIL) experiments on the dynamic test benchusing the same measurement devices for NOx and PMemissions as mentioned in section 2. Additionally, a flowmeter captures the injected fuel amount precisely. AVLInMotion is used for connecting a high fidelity vehicle

0 100 200 300 400 500 6000

20

40

60

∆ x

[m

]

∆ x� min. ∆ x� max. ∆ x

0 100 200 300 400 500 6000

10

20

velo

city [m

/s]

predecessor� ego−vehicle

0 100 200 300 400 500 600−2

0

2

time [s]

accele

ration [m

/s2]

Fig. 9. Profiles for inter-vehicle distance ∆x, velocities v,vp and accelerations a, ap of ego-vehicle and prede-cessor.

simulation environment to a real engine. The trajectoryvp for the preceding vehicle is selected according to thevelocity profile depicted in Fig. 8. The parameters of theNMPC scheme are chosen to η1 = 20, η2 = 50, η3 = 20,∆x0 = 0m and dcor = 30m.

Fig. 9 depicts the results for the inter-vehicle distance ∆xincluding the limits ∆xmin and ∆xmax, and a comparisonof the velocity and acceleration profiles v, vp and a, apof the ego-vehicle and the predecessor. Thereby it can beclearly observed that the controlled ego-vehicle shows ananticipatory and smoothed driving behavior by using thedisturbance prediction and the distance corridor.

Due to these prospective driving actions the fuel consump-tion and engine emissions can be reduced significantlycompared to the preceding vehicle as presented in Fig.10. The high values of PM emissions of the ego-vehicle atthe beginning can be explained by the fact that mediumor even low values of acceleration constitute adverse oper-ating points of the engine. Nevertheless, the avoidance ofhigh and sometimes unnecessary acceleration maneuversleads to fuel and emissions efficient driving which can beconcluded from table 1 showing the total values of fuelconsumption, NOx and PM emissions.

Table 1. Results of HIL experiments regardingtotal emission and fuel consumption values.

fuel cons. NOx PM

predecessor 263g 1 1ego-vehicle 229g 0.76 0.72

Reduction 13% 24% 28%

Regarding computation time it should be mentioned thatthe OCP is solved within few milliseconds by the ACADOToolkit which easily allowed for real-time implementa-tion for the HIL experiments. Table 2 presents the min-imum, maximum and mean computation time of NMPCiteration step.

2015 IFAC NMPCSeptember 17-20, 2015. Seville, Spain

164

0 100 200 300 400 500 6000

100

200

300

∫ fu

el co

ns.

[g]

predecessor

ego−vehicle

0 100 200 300 400 500 6000

0.5

1

∫ N

Ox [

]

0 100 200 300 400 500 6000

0.5

1

time [s]

∫ P

M [

]

Fig. 10. Comparison of integrated fuel consumption andengine emissions NOx, PM from HIL measurements

Table 2. Minimum, maximum and mean com-putation time of a NMPC iteration.

Min Max Mean

23.23ms 26.44ms 23.47ms

5. CONCLUSIONS

The paper proposes an approach for fuel and emissionsefficient cooperative adaptive cruise control using non-linear model predictive control. A disturbance predictionmodel is introduced to estimate the prospective velocityprofile of the preceding vehicle which consequently allowsanticipatory driving behavior of the ego-vehicle with theproposed control strategy. Experimental results obtainedby HIL measurements on a dynamic engine test benchshow a significant reduction of vehicle emissions ans fuelconsumption of the ego-vehicle compared to the predeces-sor whose trajectory is based on real world measurements.

Further work on this approach will cover more complextraffic situations handling more than one external trafficparticipant. Also, robustness of the controller againstfalse estimation of the predecessor’s behavior as well asvariation of the predecessor due to e.g. lane change will beof great interest in further investigations.

ACKNOWLEDGEMENTS

This work has been partially supported by the Linz Centerof Mechatronics (LCM) in the framework of the AustrianCOMET-K2 program.

REFERENCES

Asadi, B. and Vahidi, A. (2011). Predictive Cruise Control:Utilizing Upcoming Traffic Signal Information for Im-proving Fuel Economy and Reducing Trip Time. Con-trol Systems Technology, IEEE Transactions on, 19(3),707–714.

Brackstone, M. and McDonald, M. (1999). Car-following:a historical review. Transportation Research Part F:Traffic Psychology and Behaviour, 2(4), 181 – 196.

Ferreau, H.J., Ortner, P., Langthaler, P., del Re, L.,and Diehl, M. (2007). Predictive control of a real-world Diesel engine using an extended online active setstrategy. Annual Reviews in Control, 31(2), 293 – 301.

Houska, B., Ferreau, H., and Diehl, M. (2011). ACADOToolkit – An Open Source Framework for AutomaticControl and Dynamic Optimization. Optimal ControlApplications and Methods, 32(3), 298–312.

Jones, S., Huss, A., Kural, E., Massoner, A., Vock, C., andTatschl, R. (2014). Development of Predictive Vehicle &Drivetrain Operating Strategies based upon AdvancedInformation & Communication Technologies (ICT). InTransport Research Arena, Paris.

Lang, D., Schmied, R., and del Re, L. (2014). Predictionof Preceding Driver Behavior for Fuel Efficient Coop-erative Adaptive Cruise Control. SAE Int. J. Engines,7(1), 14–20.

Lang, D., Stanger, T., and del Re, L. (2013). Opportunitieson Fuel Economy Utilizing V2V Based Drive Systems.In SAE Technical Paper 2013-01-0985.

Moser, D., Waschl, H., Kirchsteiger, H., Schmied, R., anddel Re, L. (2015a). Cooperative Adaptive Cruise ControlApplying Stochastic Linear Model Predictive ControlStrategies. In to be published at European ControlConference (ECC) 2015.

Moser, D., Waschl, H., Schmied, R., Efendic, H., anddel Re, L. (2015b). Short Term Prediction of theSurrounding Traffic Velocity Using Bayesian Networks.In to be published at SAE World Congress 2015.

Ortner, P. and del Re, L. (2007). Predictive Controlof a Diesel Engine Air Path. Control Systems Tech-nology, IEEE Transactions on, 15(3), 449–456. doi:10.1109/TCST.2007.894638.

Quirynen, R., Gros, S., and Diehl, M. (2013). EfficientNMPC for nonlinear models with linear subsystems. InDecision and Control (CDC), 2013 IEEE 52nd AnnualConference on, 5101–5106.

Schmied, R., Stadlbauer, S., Waschl, H., and del Re, L.(2015a). Improving the transient emission performanceof a Diesel engine by input shaping techniques. In to bepublished at SICE Annual Conference 2015.

Schmied, R., Waschl, H., and del Re, L. (2015b). A Simpli-fied Fuel Efficient Predictive Cruise Control Approach.In SAE Technical Paper 2015-01-0296, 2015,.

Schmied, R., Waschl, H., and del Re, L. (2015c). Extensionand Experimental Validation of Fuel Efficient PredictiveAdaptive Cruise Control. In American Control Confer-ence (ACC), 2015.

Stanger, T. and del Re, L. (2013). A model predictiveCooperative Adaptive Cruise Control approach. InAmerican Control Conference (ACC), 2013, 1374–1379.

Waschl, H., Alberer, D., and del Re, L. (2012). MultiReference Model Predictive EGR Valve Control forDiesel Engines. In Proceedings of the IFAC Workshopon Engine and Powertrain Control, Simulation andModeling, volume 3, 474–481.

Xiao, L. and Gao, F. (2010). A comprehensive reviewof the development of adaptive cruise control systems.Vehicle System Dynamics, 48(10), 1167–1192.

2015 IFAC NMPCSeptember 17-20, 2015. Seville, Spain

165