1

Evaluation of Lane Departure Correction SystemsUsing a Stochastic Driver Model

Wenshuo Wang and Ding Zhao

Abstract—Evaluating the effectiveness and benefits of driverassistance systems is crucial for improving the system perfor-mance. In this paper, we propose a novel framework for testingand evaluating lane departure correction systems at a low cost byusing lane departure events reproduced from naturalistic drivingdata. First, 529,096 lane departure events were extracted fromthe Safety Pilot Model Deployment (SPMD) database collectedby the University of Michigan Transportation Research Institute.Second, a stochastic lane departure model consisting of eightrandom key variables was developed to reduce the dimension ofthe data description and improve the computational efficiency. Assuch, we used a bounded Gaussian mixture model (BGM) modelto describe drivers’ stochastic lane departure behaviors. Then, alane departure correction system with an aim point controller wasdesigned, and a batch of lane departure events were reproducedfrom the learned stochastic driver model. Finally, we assessed thedeveloped evaluation approach by comparing lateral departureareas of vehicles between with and without correction controller.The simulation results show that the proposed method caneffectively evaluate lane departure correction systems.

Index Terms—Performance evaluation, lane departure correc-tion system, naturalistic driving data, stochastic driver model,bounded Gaussian mixture model

I. INTRODUCTION

A. Motivations

S INGLE-VEHICLE road departure accounts for approxi-mately 37.4% of all fatal vehicle crashes in the United

States. Many studies have been conducted on the lane depar-ture warning system, lane keeping assistance system, and lanedeparture assistance system [1]–[3] to warn or assist driversin keeping the vehicles within the driving lane, preventingthe run-off-road crashes. These systems have the potentialto address a large proportion of serious injury and fatalcrashes. Minoiu Enache [4] et al. designed a switching steeringassistant controller for lane departure cases when drivers havea lapse of attention. Alirezaei [5] et al. developed a robustcontroller of steering assistance systems for lane departureavoidance by considering the main uncertainties affectingthe vehicle dynamics. Reagan and McCartt [6] undertook aninvestigation into the frequency of activating a lane departurewarning system and a forward collision warning system,concluding that the activation rate is much higher for forwardcollision warning than lane departure warning.

W. Wang is with School of Mechanical Engineering, Beijing Institute ofTechnology, Beijing, China, 100081, and also with Department of MechanicalEngineering, University of California at Berkeley, Berkeley, CA, US, 94706.e-mail: wwsbit@gmail.com

D. Zhao (corresponding author) is with University of Michigan Trans-portation Research Institute, Ann Arbor, MI 48109-2150, USA. e-mail:zhaoding@umich.edu

B. Related Research

A well-designed LDC system should be favored by variousdrivers. However, limited numbers of literature concern onhow to evaluate the effectiveness and benefits of these systems.Most research evaluated their LDC systems by conductingreal-life experiments. For example, Kwon and Lee [7] eval-uated two heuristic decision making strategies–a lateral offsetbased strategy and a time-to-lane crossing based strategy–forlane departure warning in real expressway experiments. Forsetting or computing the time-to-lane crossing, Mammar [8], etal. made a systematically theoretical analysis and developed acalibration method by taking many road experiments. Usually,evaluating the LDC systems requires a series of repeatedexperiments and takes researchers many resources and time,especially in the development stage of a new LDC system.

Several naturalistic field operational tests (NFOT) havebeen conducted by the University of Michigan Transporta-tion Research Institute, sponsored by the National HighwayTraffic Safety Administration [9], [10]. However, this typeof evaluation method ordinarily has a very high cost suchthat it is only suitable to test and evaluate the final product,but not affordable to be used in the development procedure.Therefore, approaches that can reuse the NFOT data on newlydeveloped devices will be of great benefits. Recently, thesimulation-based evaluation method has become popular [11]–[14] in the safety of automated vehicles. For example, thevehicle-in-the-loop method has been developed to investigatedriver behaviors and evaluate the ADASs [15] by combiningreal driving experience with the replicability and safety ofsimulators, which enables the safe and controlled replicationof specific traffic situations. Another approach is based ondriving simulators [16]–[18], in which the driving environmentis usually repeatable. Therefore, to make lane departure eventsas similar to what it might be in the real world, a mathematicalmodel is developed in this paper, capable of regeneratingdrivers’ lane departure behaviors. The lane departure eventsgenerated from the developed model can be used to test LDCsystems, compare designs, and evaluate their social benefits.This model may also facilitate research to investigate thephysiological and cognitive behaviors of human drivers.

C. Contributions

In this paper, we propose a simulation-based frameworkto evaluate the LDC systems based 529,096 lane departureevents collected from the real world naturalistic driving. Theevaluation procedures are shown in Fig. 1. First, a stochasticlane departure model is built based on the bound Gaussian

arX

iv:1

702.

0577

9v1

[cs

.SY

] 1

9 Fe

b 20

17

2

Fig. 1: Evaluation procedure using a stochastic lane departuremodel based on naturalistic driving data.

mixture (BGM) model using a very large quantity of natural-istic driving data. In the trained model, we use a dimensionreduction method and apply eight statistical variables rep-resenting a lane departure behavior. Thus, from the trainedstochastic lane departure model, we extract and regeneratethe lane departure event being as similar to what the driverwill do in the real world. The lane departure events generatedfrom the stochastic driver model are then used to test andevaluate the LDC systems. When a vehicle is going to driftand cross the lane marker, the LDC systems will automaticallycontrol the power steering and bring the vehicle back to thecenter of the driving lane. In this way, we can repeatedly testand evaluate different LDC systems in various lane departurebehaviors. Overall, our contributions are that (1) we proposea new simulation-based evaluation framework to reuse thenaturalistic driving data (Fig. 1), (2) a stochastic lane departuremodel is developed with dimension reduction, and (3) theBGM model is introduced to characterize the statistical featureof the departure behavior.

D. Paper Organization

The remainder of this paper is organized as follows. SectionII introduces the structure of the stochastic driver model forlane departure behaviors. Section III describes a model fittingapproach for the stochastic driver model. Section IV showsthe vehicle dynamics model and the designed controller forevaluating the proposed method. Section V discusses andanalyzes the experiment results and Section VI gives a furtherconclusion of this research.

II. STOCHASTIC LANE DEPARTURE MODEL

The procedure of generating lane departure behavior isshown in Fig. 2. All the naturalistic driving data were collectedfrom the Safety Pilot Model Deployment (SPDM) database[19]. In this section, we present approaches for Step 2 andStep 3 in Fig. 2.

A. Key Variables Selection

The characteristics of lane departure behavior vary acrossdrivers, even for a single driver with different driving timesand locations. For example, due to different driving styles andtheir diversity in physical and mental states [9], the lasting time

Step1: Collected the naturalistic lanedeparture events from SPDM database

Step2: Identify key variables (lateraldeparture distance, speed, curvature)

Step3: Establish stochastic model (describethe key variables and human uncertainties)

Step4: Fitting model parameter using theBGMM approach

Step5: Reproduce lane departure eventswith samples of model parameters

Fig. 2: Procedure to build the lane departure model.

𝑋

𝑌Lane center

1/𝜌  (𝑡)[𝑦 0 , 𝑣 0 , 𝜌 0 ]

[𝑦 𝑇 , 𝑣 𝑇 ,𝜌 𝑇 ]𝑣(𝑡)

𝑦(𝑡)

Begin of lanedeparture

End of lanedeparture

Fig. 3: Illustration of a left lane departure event.

of a lane departure event differs. Fig. 3 illustrates the left lanedeparture event, in which three key variables are presented.Fig. 4 draws a few of left/right lane departure event with threevariables, including lateral departure distance y, velocity v,and curvature ρ. In this research, these three key variables(shown in Fig. 3) are selected to describe a lane departureevent and discussed as follows:• Lateral Departure Distance (y): Lateral departure dis-

tance y is defined as the distance from the vehicle’sleft/right side to the left/right lane edge when the vehicleis driving toward left/right. If vehicles are approaching toright lane edge, y < 0, and otherwise, y > 0.

• Vehicle Speed (v): Vehicle speed has a great influence onthe strategy of determining whether the LDC system willbe activated. A higher speed normally allows less time topull the vehicle back to the lane center for drivers.

• Lane Curvature (ρ): According to [8], road curvatureis a very important variable to determine and describe adriver’s lane departure behavior since the road curvaturehas a direct impact on the time-to-lane crossing.

B. Dimension Reduction

Our goal is to build a model that can generate lane departureevents being statistically equivalent to the event collectedfrom naturalistic driving data. If the departure duration Tis 5 s with sampling time Ts being 0.1 s, we will get3× (T/Ts + 1) = 153 data points to fully describe the threevariables y, v, and ρ. This dimension is normally too highand unnecessary to build a stochastic lane departure model.

3

Travel distance x[m]0 20 40 60 80L

ate

raldispla

cem

ent

y[m

]

-0.4

-0.2

0

0.2

0.4

y > 0, left lane departure

y < 0, right lane departure

(a) Lateral departure distance

Time t[s]0 1 2 3 4 5

Veh

icle

spee

dv[m

/s]

10

15

20

25

30

35

(b) Velocity

Time t[s]0 1 2 3 4 5

Road

curv

atu

re;[1

/m

] #10-4

-20

-15

-10

-5

0

5

(c) Road curvature

Fig. 4: Lateral offset examples during lane departure.

Therefore, it is essential to find a flexible way that can employall the dataset at a low computational cost. To achieve this,we propose a polynomial fitting-based approach to reduce thedata dimension by extracting eight key features of the threevariables while reserving the model uncertainty, given by:

• Lateral Departure Distance: As illustrated in Fig. 4(a),y can be approximated as a second order polynomialfunction of the longitudinal travel distance x with theaddition of an error term.

y(t) = y(t) + εy(t) (1)

y(t) = −4dyd2x

(x(t)− dx

2

)2

+ dy (2)

where dx is the longitudinal travel distance during thedeparture, dy represents the lateral departure calculatedby the least square method, and εy(t) is the error term.The variance of human driving is captured by the standarddeviation of εy(l), calculated from

σy =

√√√√ 1

L− 1

L∑l=1

|εy(l)− εy|2 (3)

where L is the number of samples in one lane departureevent, and εy(l) = y(l)− y(l) represents the error of thelth sample of the lane departure event. In the followingpaper, we use t to represent continuous time starting from0 to T and use l as the index of the discrete sample timestarting from 1 to L, and εy =

∑Ll=1 εy(l).

• Velocity: From Fig. 4(b), we know that the vehicle speedonly changes slightly in a lane departure event. Withoutconsidering emergency braking behaviors, as such thevelocity in a lane departure event can be approximatedas a linear function of time, represented by

v(t) = v(t) + εv(t) = a(t− T/2) + v + εv(t) (4)

where v = dx/T is the average speed. The averageacceleration a is estimated using the least square method.Similarly, human uncertainties in velocity can be calcu-lated from

σv =

√√√√ 1

L− 1

L∑l=0

|εv(l)− εv|2 (5)

• Curvature: Similar to the vehicle speed, the lane curva-ture in a departure event only changes slightly and canalso be modeled as a linear function of time:

ρ(t) = ρ(t) + ερ =∆ρ

Tt+ ρ0 + ερ (6)

where ρ0 is the initial curvature, ∆ρ is the curvaturechange from the start to the end of a departure event.To smooth the curvature data, we use linear regressionto estimate ρ0 and ∆ρ, such that

∑Ll=1 |ρ(l) − ρ(l)|2 is

minimized.The advantages of this model with dimension reduced are

that (1) it reduces the dimensions of the original data whilecapturing the stochastic variance of human driving behaviorsand (2) each of its parameters has a clearly defined physicalmeaning.

Fig. 5 provides an example of fitting results by using the di-mension reduction approach we proposed. We note that for thecase with T = 4.1 s in Fig. 5, the approximated approach cancapture the lane departure features by using only 8-dimensionfeatures (i.e., T , dy , σy , v, a, σv , ∆ρ, and ρ0), instead of using126-dimension features (3 × (T/Ts + 1) = 126). Therefore,the proposed dimension reduction approach greatly improvesthe computational efficiency with reserving the uncertaintiesof human driving as well as catching the key features of thethree variables, i.e., y, v, and ρ.

4

travel distance x[m]0 10 20 30 40 50 60 70

lanedeparture

y[m]

-0.6

-0.4

-0.2

0

dy = −0.5488[m]σy = 0.0604[m]

Raw dataFitting model

time t[s]0 1 2 3 4

speedv[m/s]

15

15.5

16

16.5T = 4.1000[s]v = 15.6250[m/s]a = 0.0370[m/s2]σv = 0.1807[m/s]

Raw dataFitting model

time t[s]0 1 2 3 4

curvature

ρ[m

/s]

×10-3

-5

-4

-3

-2

T = 4.1000[s]∆ρ = −2.2× 10−3[1/m]ρ0 = −2.1× 10−3[1/m]

Raw dataFitting model

Fig. 5: Example of fitting results and identified parameters fora right lane departure event by using our proposed method.

III. LEARNING WITH NATURALISTIC DRIVING DATA

In this section, we use the BGM model to capture thestochastic features of these 8 variables mentioned in SectionII. And then we can stochastically extract and sample the 8variables from the trained BGM model, thereby generatingvarious lane departure events.

A. Naturalistic Lane Departure Events

The naturalistic driving data used in this research areextracted from the SPMD database. It recorded naturalisticdriving of 2,842 equipped vehicles in Ann Arbor, Michiganfor more than two years. As of April 2016, 34.9 millionmiles were logged. We used 98 sedans to run experiments andcollect the real on-road data. The vehicle is equipped with dataacquisition systems and Mobileyer [14]. The Mobileye getsthe driving data such as lane curvature, lateral displacementwith respect to lane marks, and lane tracking measures aboutthe lane delineation both from the painted boundary linesand the road edge, etc. The global navigation satellite system(GNSS) gets the global position (latitude and longitude) andthe GNSS time. The CAN-Bus signals including the vehiclespeed, acceleration, throttle opening, braking force, and enginespeed are obtained. To ensure consistency of the used dataset,we extract data from the database by the following criteria:

T [s]2 4 6 8 10

p.d.f

0

0.05

dy [m]-0.8 -0.6 -0.4 -0.2

p.d.f

0

0.02

0.04

σy [m]0 0.1 0.2 0.3

p.d.f

0

0.02

0.04

v[m/s]10 20 30 40

p.d.f

0

0.02

0.04

a[m·s−2]-1 0 1

p.d.f

0

0.05

0.1

σv [m/s]0 0.1 0.2 0.3

p.d.f

0

0.2

0.4

∆ρ[(m·s)−1]×10-3-1 0 1

p.d.f

0

0.1

0.2

ρ0[m−1] ×10-3-2 0 2

p.d.f

0

0.1

0.2

Fig. 6: Marginal distributions of right lane departure variables.

1) The duration of each event should be in the range of 0.5s to 10 s;

2) The average velocity of each event should be larger than5 m/s. This limitation excludes the traffic jam and thestop-and-go behaviors.

In total, 529,096 lane departure events (249,798 left and279,298 right lane departure events) are identified from 118drivers over the last four years.

B. Variable Fitting Approach

We aim to develop a statistical model to describe the jointdistribution of the eight parameters. Let ξ(n) = [T (n), d(n)

y ,σ

(n)y , v(n), a(n), σ(n)

v , ρ(n)0 , ∆ρ(n)]> ∈ R8×1, where ξ(n)

describes the nth lane departure event and n = 1, 2, ..., N isthe index of departure events. The marginal distribution of ξfor left lane departure events and right lane departure eventsare shown Fig. 6 and Fig. 7, respectively. We can see thateach variable follows an unique distribution, especially forthe average of vehicle speed v and the initial curvature ofdeparture ρ0. From the joint distribution between duration Tand departure distance dy as shown in Fig. 8, we can seethat there is clear dependence between variables. In addition,from Fig. 6 and Fig. 7 it can be seen that the distributionsof variables T , σy , and σv have bounded features. Therefore,a flexible probability density function (p.d.f.) is required tomodel the multi-variate distribution. In this paper, we selectand discuss the BGM model to describe the joint distributionof the 8 variables as follows.

5

T [s]2 4 6 8 10

p.d.f

0

0.05

0.1

dy [m]0 0.5 1

p.d.f

0

0.02

0.04

σy [m]0 0.1 0.2 0.3

p.d.f

0

0.05

v[m/s]10 20 30 40 50

p.d.f

0

0.05

a[m·s−2]-1 0 1

p.d.f

0

0.05

0.1

σv [m/s]0 0.1 0.2 0.3

p.d.f

0

0.2

0.4

∆ρ[(m·s)−1]×10-3-1 0 1

p.d.f

0

0.1

0.2

ρ0[m−1] ×10-3-2 0 2

p.d.f

0

0.1

0.2

Fig. 7: Marginal distributions of left lane departure variables.

Fig. 8: Joint distributions of duration and maximum departuredistance for both left and right lane departure cases.

1) Structure of the BGM model: In light of its flexibilityand ease of training, the Gaussian Mixture Model (GMM) havebeen widely used in many applications such as speech recog-nition [20], pattern recognition [21], and driving behaviors[12], [22]. In terms of driver behavior, some feature boundariesusually exist because of the physical limitations of variables,which thereby makes traditional GMM approaches be difficultto perfectly fit these boundaries. For example, the duration ofa departure event should be larger than 0.5 s and less than 10

s, and the vehicle speed should be positive, as shown in Fig. 6and Fig. 7. To deal with this issue, in this research, we appliedthe BGM model-based method to incorporating the physicalboundaries. One of key advantages of the BGM model is thatit considers the variable boundaries while also preserving atractable form when using the expectation-maximization (EM)algorithm to train the model.

The probability density function of a BGM model can beexpressed as [20]

fBM(ξ) =fGM(ξ)∫ bu

blfGM(u)du

(7)

where fGM(ξ) is a normal GMM, given by

fGM(ξ|Θ) =

K∑k=1

πkgk(ξ;θk) (8)

where πk ∈ [0, 1] are mixing weights with∑k πk = 1,

gk is the kth D-dimensional Gaussian distribution compo-nent (In this paper, D = 8) parameterized by θk =[µk,Σk], Θ = [π1, ..., πK ,θ1, ...,θK ]. Here we assumethat the boundary is a hyper-rectangle in RD×1 with twovertices bu = [Tu, duy , σ

u, vu, au, σuv , ρu0 ,∆ρ

u]> and bl =[T l, dly, σ

l, vl, al, σlv, ρl0,∆ρ

l]> on the diagonal opposites

bl < ξ(n) < bu (9)

and computed by

?u = max{?(n), n = 1, 2, · · · , N}?l = min{?(n), n = 1, 2, · · · , N}

(10)

where ? ∈ [T, dy, σy, v, a, σv, ρ0,∆ρ]. By incorporating theboundary function, it can be derived that fBM is also a mixture

fBM =

K∑k=1

ηkfk(ξ) (11)

with mixing weights ηk and component density functions fk:

ηk = πk

∫ bu

blgk(u)du∫ bu

blfGM(u)du

(12)

fk(ξ) =gk(ξ)∫ bu

blfGM(u)du

(13)

2) Parameter Estimation for BGM Model: The best pa-rameter Θ∗ for the BGM model is the ‘most likely’ giventhe data observed, i.e., the parameter that maximizes thelikelihood function of the BGM model. We assume that thedata vectors are independent random variables, and therebythe log-likelihood function of fBM can be expressed as [23]

LB(Θ) = ln∏n

∑k

znk ηkfk(ξn)

=∑n

∑k

znk [ln ηk + ln fk(ξn)− ln

∫ bu

bl

fk(u)du]

(14)

6

Number of BGMM component0 5 10 15 20

BIC

×106

-8

-7

-6

-5

Left lane departure

Chan

ge ra

te o

f BIC

×105

0

5

10

15

Number of BGMM component0 5 10 15 20

BIC

×106

-8

-7

-6

-5

Right lane departure

Chan

ge ra

te o

f BIC

×105

0

5

10

15

Fig. 9: BIC with different numbers of BGM model componentsfor left and right lane departure data.

The expectation of LB(Θ) can be calculated from

QB(Θ(i+1);Θ(i)) = E[LB(Θ)|ξ1:N ;Θ(i)]

=∑n

∑k

〈znk 〉[

ln ηk + ln fk(ξn)

− ln

∫ bu

bl

fk(u)du] (15)

where the latent variable 〈znk 〉 := P(znk = 1|ξn).Due to the function’s nonlinearity with respect to model

parameter Θ, it is extremely impossible to directly differ-entiate and maximize the likelihood function. To solve thisproblem, as shown in [24], an extended EM iteration approachis employed and given by

ηk =1

N

∑n

〈znk 〉 (16)

µk =

∑n〈znk 〉ξn∑n〈znk 〉

−mk (17)

Σk =

∑n〈znk 〉(ξn − µk)∑

n〈znk 〉+Hk (18)

where

mk =M1(0,Σk; [bl − µk, bu − µk]) (19)

Hk = Σk −M2(0,Σk; [bl − µk, bu − µk]) (20)

with M1 and M2 represent the first order and second ordermoment generated function of fBM. Update (15) – (20) foreach iteration and check the changed value of (14). When the

changed value of (14) for two adjacent iterations is smallerthan a predefined threshold ε = 1.0×10−6, then stop iterationand output the optimal model parameter Θ∗.

The component number K of the BGM model is chosenbased on numerical analysis of the Bayesian InformationCriterion (BIC) [25]. A large value of K will increase thecomputational cost of training and a small value of K is unableto fully describe the underlying features of data. As shown inFig. 9, the BICs for both of left and right lane departure datadecrease very slowly and starts to oscillate when the numberof components is greater than 10. Therefore, we chose K = 10in this paper by considering the computational complexity andmodel accuracy.

IV. EVALUATION OF LANE DEPARTURE CORRECTIONSYSTEMS

An LDC system is designed to show the benefits of theproposed evaluation framework. First, we show how to regen-erate lane departure events from the learned BGM model. Andthen, we design a controller for an LDC system based on abicycle-vehicle model.

A. Regeneration of Lane Departure Events

A batch of lane departure events is generated from the BGMmodel using a simple sampling method. Algorithm 1. providesa detailed procedure for regeneration of lane departure event,and discusses as follows:

1) We reproduce the lane departure events that have thesame statistical characteristics as the lane departuredata collected from on-road experiments. The Matlabfunction (i.e., random) for generating random numberwas directly employed by setting the random number asNgen = 105, see Step 2 in Algorithm 1;

2) Applied (9) to extracting the available events generatedfrom the learned BGM model;

3) Reproduce lane departure events using the proposedmodel (1)–(6).

By following Algorithm 1, we generate a wide range of lanedeparture events.

Algorithm 1 Steps of Reproducing Lane Departure Events1: Initialize: Learn the BGM model and save as BGMMLearned2: Primary Samples: Generate the random values that have

the same distribution with BGMMLearned by using Matlabfunction random

BGMMSample = random(BGMMLearned, Ngen)3: Check Available Values: Extract the available vectors

using the criteria (9),LDEVariable = MyFilter(BGMMSample, Criteria(9))

4: Generate Lane Departure Events: Based on filteredvariables, we can generate the lane departure events usingthe LaneDepModel (1)–(6):

LDEs = MyGenerator(LDEVariable, LaneDepModel)5: Return: Output the reproduced lane departure events

LDEs.

7

Fig. 10: Bicycle model and the relevant variables on a curvepath.

B. Vehicle Dynamics Model

In this paper, a simple bicycle-vehicle model (Fig. 10) isapplied to validating our proposed evaluation approach. Fromthe statistical results in Fig. 6 and Fig. 7, we note that duringthe departure behavior, the curvature, curvature change, andthe vehicle speed change are very small, which means thevehicle side slip angle will be very small. When vehicle speedchanges slightly and the side slip value is small, a linearvehicle model can be employed. As shown in Fig. 10, asimplified vehicle model is used, where two front wheels andtwo rear wheels are lumped together, respectively. ψ is headingangle and ψl is the tangent direction of the lane. We definethe heading error eψ and offset error ey as

eψ = ψ − ψley = y − wv

2+wl2

(21)

where wv and wl are the vehicle width and the lane width,respectively. Thus we can write the vehicle dynamic model ina state space form:

x(t) = Ax(t) +Bδ(t) +Eψl(t), (22)

where

A =

0 1 0 0

0 − 2Cαf+2CαrMvx

2Cαf+2CαrM − 2Cαf lf+2Cαrlr

Mvx0 0 0 1

0 − 2Cαf lf−2CαrlrIzvx

2Cαf lf−2CαrlrIz

− 2Cαf l2f+2Cαrl

2r

Izvx

B =

0

2Cαfm0

2Cαf lfIz

, E =

0

− 2Cαf lf−2Cαrlrmvx

− vx0

− 2Cαf l2f+2Cαrl

2r

Izvx

where x(t) = [ey, ey, eψ, eψ]> ∈ R4×1, A ∈ R4×4, B ∈R4×1, E ∈ R4×1, Cαf and Cαr are the tire stiffness, lf andlr are the longitudinal distance from center of gravity to thefrontal axle and rear axle, respectively, m is the total mass,Iz is the inertia of z axis, and δ, as the model input, is thesteering angle of the front wheel.

C. Controller Design

The main goal of designing a controller is to validate theefficiency of our proposed stochastic driver model. Thus, wedesign an aim point controller [26] for the steering system tohelp driver pull the vehicle back to lane center. We define thepreview orientation error elpψ (Fig. 10) as

elpψ = ψ − ψlpl (23)

and assign

∆ψl = ψl − ψlpl (24)

Substituting (24) to (23), we have

elpψ = ψ − (ψl −∆ψl) = eψ + ∆ψl (25)

Considering the control law

δ = Kyey +Kψelpψ (26)

and letting F = [Ky, 0,Kψ, 0] and G = Kψ , we have

δ = Kyey +Kψeψ +Kψ∆ψl

= Fx+G∆ψl(27)

The LDC system kicks in when a predefined threshold isvalid. In this paper, we activate the designed controller wheny < yRs < 0 for the right lane departure or y > yLs > 0for the left lane departure is valid, where yRs and yLs are boththe predefined thresholds. We set |yLs | = |yRs | = ys = 0.2m. After the vehicle is pulled back to around the center ofthe driving lane, the controller will stop work. Based on thediscussion above, we rewrite (22) as follows

x(t) = Acx(t) +BcΨ(t), (28)

where Ac = A + BF , Bc = [E,BG], Ψ(t) = [ψl(t),∆ψl(t)]

>. Substitute (27) into (28), we obtain the close loopform

ψl(t) = vx(ts)c(t) =vx(ts)∆c

Tt+ vx(ts)c0 (29)

∆ψl(t) =

∫ t+Tlp

t

ψl(t)dt = A∆ψlt+B∆ψl (30)

where A∆ψl =∆c·Tlp·vx(ts)

T and B∆ψl =∆c·T 2

lp·vx(ts)

2T +vx(ts) · c0 ·Tlp. The initial condition of the close loop controlis ey(ts) = y(ts) + (wl −wv)/2 and eψ(ts) = arctan(

vy(t)vx(t) ),

where vy(ts) =dey(t)dt |t=ts , ts is the starting time of lane

departure happening. Table I gives the parameters used in thesimulation.

V. ANALYSIS AND DISCUSSIONS FOR SIMULATIONRESULTS

We analyze our proposed evaluation framework from twoaspects: (1) statistical analysis of reproduced lane departureevents and (2) evaluation of the controller using the reproducedlane departure events.

8

TABLE I: Simulation Parameters

Var Unit Value Var Unit ValueCαf N/rad 80,000 lr m 1.47Cαr N/rad 80,000 Iz kgm2 3,344lf m 1.43 M kg 1,000Tlp s 2 Dy m 0.5Ts s 0.1 wl m 3.6Ky rad/m -0.005 wv m 1.9Kψ rad/rad -0.2 ys m 0.2

A. Reproduction Efficiency for Lane Departure Events

We evaluated the efficiency to reproduce lane departureevents. Fig. 11 shows the statistical results of available eventsin all lane departure events when we repeatedly run the lanedeparture model for 100 times. We find that about 7% and14.5% of 105 reproduced events satisfy the condition (9)for right and left lane departure behaviors, respectively. Thisapproach generates lane departure behaviors at a relativelylow proportion, but it computes very fast and takes totallyless 2 s to generate 105 lane departure events using MatLabin the laptop with a processor of 2.5 GHz Intel Core i7.Our proposed dimension reduction approach in Section IIsignificantly reduces the computational time.

B. Controller Evaluation

Fig. 12 illustrates the left lane departure events (green solidline) generated from the stochastic model and the trajectories(red dot line) of vehicle’s left front wheel with the designedcontroller. The red point represents the trigger point where thecontrol condition is valid, i.e., y < −0.2 m for right departurebehavior or y > 0.2 m for left departure behavior.

Fig. 13 provides two examples of simulation results for rightand left departure behaviors with/without the controller. Theblack solid line represents the lane boundary and the blackdash line is the center of the driving lane. The blue line is thelane departure trajectory generated from the stochastic lanedeparture model. The green dash line is the fitted model using(1)–(6), and the red line is the vehicle trajectory with thedesigned controller. In Fig. 13, when the condition of vehiclelateral displacement is valid (i.e., yL > ys or yR < −ys,denoted by red circles in Fig. 13), the controller is triggered,

Number of the available event600 700 8000

5

10

15

Right lane departure

Number of the available event1300 1400 1500 16000

5

10

15

Left lane departure

Fig. 11: Statistical results for available lane departure eventsgenerated from 105 events.

𝑋

𝑌Controlleris triggered

Left wheel trajectory withoutcontroller

Left wheel trajectory withcontroller

Lane center

Departure area without controllerDeparture area with controller

Fig. 12: Illustrations of vehicle trajectories with and withoutthe designed controller.

Time [s]0 1 2 3 4 5 6

Lateral

diplacementy[m

]

-4

-2

0

2

4

6T = 5.187[s], dy = -0.204[m]σy = 0.027[m], v = 39.191[m/s]a = 0.314[m/s2], σv = 0.024[m/s]∆ρ = 1.658e-05[1/m], ρ0 = 3.232e-05[1/m]

Control No control Fitted model

Trigger point

(a) Right departure cases

Time [s]0 1 2 3 4 5 6

Lateral

diplacementy[m

]

-4

-2

0

2

4

6T = 5.363[s], dy = 0.194[m]σy = 0.033[m], v = 29.783[m/s]a = -0.091[m/s2], σv = 0.076[m/s]∆ρ = 1.82119e-05[1/m], ρ0 = 8.150e-07[1/m]

Control No control Fitted model

Trigger point

(b) Left departure cases

Fig. 13: The comparison results of the lane departure eventswith and without controller.

thereby assisting the driver in steering the vehicle back tothe center of the driving lane. It is obvious that the proposedstochastic lane departure model can reproduce the lane depar-ture events similar to these in the real world, and then can beused to evaluate a controller performance for LDC systems ata low cost.

To further show the benefits of our proposed evalua-tion framework, the area between the trajectory of vehicle’sleft(right) wheel for left(right) lane departure behaviors andlane boundary for each departure event is defined and com-

9

Number of generated event (N )0 50 100 150 200

Mea

nofare

a(S

)

0

0.2

0.4

0.6

0.8

1RLD & controlLLD & controlRLD & no controlLLD & no control

Distribution0 0.2 0.4 0.6 0.8

Mea

nofare

a(S

)

0

0.2

0.4

0.6

0.8

1RLD & controlLLD & controlRLD & no controlLLD & no control

Fig. 14: The statistical results for different amounts of lane departure event, including left lane departure (LLD) event andright lane departure (RLD) event.

puted by

S =

∫ tend

tstart

|y(t)|dt (31)

where tstart and tend are the starting and ending time of thedeparture behaviors, respectively, with a smaller value of Sindicating that the vehicle is tracking the lane center better.We use (31) to evaluate the controller performance for leftand right departure behaviors. The evaluation metric (31) maynot be the most accurate approach to measure the effectivenessof the LDC systems, which may need to consider a variety ofaspects such as yaw stability, driving speed, road conditions.However, since the proposed method provides the kinematicinformation of the vehicle, it can be easily extended to includethe aforementioned factors once the extra data is available.

Fig. 14 shows the statistical results of vehicles with/withoutthe designed controller with respect to different numbers oflane departure events, including right lane departure and leftlane departure cases. The average values of the area (S) forboth right and left lane departure events with or withoutcontroller are convergent. Drivers usually prefer to take a leftlane departure when driving as the mean of the area (S) forright lane departure behaviors without controller (red line) issmaller than that for left lane departure behaviors (black line).

Fig. 15 provides the statistical simulation results of 200 leftlane departure events and 200 right lane departure events thatare both randomly produced from the stochastic lane departuremodel. The bar represents the mean value and the red verticalline represents the standard deviation of S. It can been seenthat our proposed method can cover various lane departureevents and that the controller can help drivers steer vehiclesback to the lane center when drivers depart away from the lanecenter. Vehicles with LDC systems can reduce the departureareas S by 44.13% and 57.31% for right and left departurebehaviors, respectively.

C. Further Discussions and Future Work

The proposed lane departure model can capture the un-certainties of human driving and improve the computational

Left departure Right departure

Mea

nand

standard

dev

iation

ofare

as

S

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Reduced

by44.13%

Reduced

by57.31%

Without controlWith control

Fig. 15: The mean and standard deviations of departure cover-ing areas for 200 left and right departure events, respectively.

efficiency. In this paper, we applied the stochastic driver modelto evaluating LDC systems. When the departure behavioroccurs, the controller is activated and takes the place of humandriver’s control. In addition to this application, the proposeddriver model can also be used to

1) assess the reliability and acceptability of LDW systemswith different designed waring strategies at a low cost;

2) investigate into the influence of different ADASs onhuman drivers’ behaviors such as decision making andreactions;

3) develop a driving simulator, enabling the lane departurebehaviors of other vehicles to be highly similar to thosein the real world.

Therefore, the work presented in this paper has the potential

10

to be widely used in the evaluation of autonomous vehicles.

VI. CONCLUSIONS

In this paper, we propose a framework for evaluating lanedeparture correction (LDC) systems based on a stochasticdriver model using a huge amount of naturalistic driving data.A stochastic driver model that can reproduce various lanedeparture events as similar to what drivers will do in real-lifedriving situations is developed by considering the uncertaintiesof human driving. In the stochastic driver model, in order toimprove the computational efficiency, we propose a dimensionreduction method using a polynomial function with the addi-tion of stochastic terms characterizing uncertainties in humandriving. And then, we apply the bounded Gaussian mixturemodel to fitting the data with incorporating the boundedcharacteristics of physical variables. Last, to show the benefitsof our proposed evaluation framework, a controller is designedand applied to a bicycle-vehicle model. The simulation resultsshow that the controller can assist a driver in steering vehiclesback to the center of the driving lane when departure behavioroccurs. This supports that the proposed stochastic driver modelis an efficient way to evaluate LDC systems at a low cost.

The proposed approach can be generalized to evaluate otherADASs such as the autonomous emergency braking system orthe lane change assistance system. It also has the potentialto evaluate autonomous vehicles by simulating the ambientstochastic driving environment.

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Wenshuo Wang received his B.S. in TransportationEngineering from Shandong University of Tech-nology, Shandong, China, in 2012. He is a Ph.D.candidate in Mechanical Engineering at the BeijingInstitute of Technology (BIT). He is a currentlyvisiting scholar in the School of Mechanical Engi-neering, University of California at Berkeley (UCB).He conducts research under the supervision of Prof.Junqiang Xi (BIT) and Prof. Karl Hedrick in theVehicle Dynamics & Control Lab, University ofCalifornia at Berkeley. His research interests include

vehicle dynamics control, adaptive control, driver model, human-vehicleinteraction, recognition and application of human driving characteristics.His work focuses on modeling and recognizing drivers behavior, makingintelligent control systems between human drivers and vehicles.

11

Ding Zhao received his Ph.D. degree in 2016 fromthe University of Michigan, Ann Arbor. He is cur-rently a Research Fellow in the University of Michi-gan Transportation Research Institute. His researchinterests include the evaluation of connected andautomated vehicles, vehicle dynamic control, driverbehaviors modeling, and big data analysis.