Yubin Xi, Matthew Crisler A Review of Lane Change Definiti.docx

Yubin Xi, Matthew Crisler
A Review of Lane Change Definitions and 1
Identification Methods 2
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Yubin Xi 7
Graduate Student, Department of Automotive Engineering,
Clemson University International Center for 8
Automotive Research, Greenville, SC 29607 USA 9
E-mail: [email protected] 10
Phone: (864) 325-2881 11
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Matthew Crisler, PhD (Corresponding Author) 13
Research Specialist, Department of Automotive Engineering,
Clemson University International Center for 14
Automotive Research, Greenville, SC 29607 USA 15
E-mail: [email protected] 16
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4 Research Dr. 20
Clemson University International Center for Automotive
Research (CU-ICAR) 21
Greenville, SC 29607 22
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Submitted: November 15, 2012 24
Word Count: 6257 25

Figures and Tables Count: 2*250=500 26
Total Words: 6757 27
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Submitted for presentation at the Transportation Research Board
92th Annual Meeting and inclusion in 36
conference proceedings, Washington D.C., January 2013 37
Yubin Xi, Matthew Crisler 1
ABSTRACT 1
Lane changes are challenging maneuvers and represent an
important component of traffic research. 2
Significant efforts have been spent on lane change related
research, and various models have been 3
developed to study lane change behavior from different
perspectives. In order to identify lane change 4
maneuvers from time series data, researchers have been using
different lane change definitions and 5

identification methods, which makes the conclusions drawn
from their research dependent on the author’s 6
choice of definition or method. This article reviews lane change
definitions and a collection of 7
identification methods, provides a summary of the existing
literature and offers information relevant to 8
the selection of a definition or identification method. 9
10
INTRODUCTION 1
Traffic crash data has shown that the lane change is a
challenging driving maneuver and thus has been an 2
important traffic research object (1,2). A large number of
studies address lane change related issues from 3
various perspectives. Many of these studies involve efforts to
identify and extract lane change segments 4
from time series data such as evaluation of empirical traffic
data, development of driver assistance system 5
or reproduction of lane changes for traffic flow models. In order
to allow for comparisons across multiple 6
studies, it is important that researchers have consistent methods
to define and segment lane changes since 7
the lane change segments captured from the collected data can
impact the research outcomes. Two major 8
factors might affect the ability to consistently interpret and
compare the results of research involving lane 9
changes. First, authors might use different definitions of lane
changes. At the level of individual research 10
projects, the definition is valid for a specific study as long as it
enables capturing the desired information 11
about the lane change maneuver. However, varied

interpretations of the scope of lane change maneuvers 12
could produce significant variability in results. For example,
some studies have addressed the time-course 13
of a lane-change maneuver (3-10); however, the duration of lane
change maneuvers in these studies was 14
not consistently defined. This makes comparisons across studies
difficult. Establishing a standard lane 15
change definition is beyond the scope of this paper; however, in
order to facilitate understanding lane 16
change maneuvers, lane change definitions utilized in the extant
literature will be reviewed and 17
characterized. 18
In addition to varied definitions, the methods adopted for lane
change identification also vary 19
widely. One common reason for the difference is that many
researchers developed the identification 20
methods based on different definitions or without explicitly
referring to an existing definition. Another 21
reason is that the availability of input variables used to identify
lane changes varies between studies. This 22
is partially due to different data sources. Data were collected
from different experimental environments, 23
(traffic simulation, driving simulator, instrumented vehicle or
naturalistic traffic recording, etc.) and each 24
environment produces different variable types. For example,
drivers’ head and eye movement and 25
steering input data could be available in instrumented vehicles
or driving simulators, but are not available 26
when using traffic simulation or naturalistic traffic recording.
Vehicle dynamics variables (velocity, 27
acceleration, yaw angle, etc.) are more readily accessible using
driving simulators, instrumented vehicles 28
and traffic simulation. In cases when the identification process
in one study involves variables that are 29
unavailable or unused in other studies, comparison across

different studies becomes difficult. Even if the 30
data were from similar experimental settings, some factors may
still influence the availability of data such 31
as the simulation software capabilities or the level of
instrumentation. 32
In order to allow researchers to address issues associated with
lane change behavior consistently, 33
this article will review the existing literature related to the
definition and identification of lane change 34
maneuvers. In this review, a collection of lane change
definitions will be described. In addition, several 35
identification methods will be presented, providing information
for those who need to identify lane 36
change segments from time-series data. Since the vast majority
of papers reviewed here were not 37
explicitly focused on defining or segmenting lane changes, their
research objectives and methodologies 38
will be briefly summarized to provide appropriate context. In
addition to providing a review of existing 39
lane-change definitions and identification methods, an example
application of lane-change identification, 40
driver assessment and training, is described in the context of the
current review. 41
LANE CHANGE DEFINITIONS 42
When it comes to research on lane change maneuvers, it is often
the case that there is no standard 43
definition that has been consistently adopted by researchers.
Therefore when they identify driver’s lane 44
change maneuvers from time-series data, the same lane change
could be represented as different segments. 45
This makes research difficult to compare and replicate and
further reduces the research credibility (11). 46
Therefore, in order to study lane change maneuvers, it is
important to address how lane change maneuvers 47

have been defined. In this section a group of lane change
definitions serving different purposes will be 48
described. 49
It is worth noting that the lane change duration can be divided
into the preparation/decision phase 1
and the execution phase. The lane change preparation/decision
phase refers to the period of time during 2
which the driver initiates the desire to change lanes and is
gathering information on feasibility of 3
changing lanes. Normally there is no noticeable and deliberate
steering activity during this phase. The 4
scope of this review is limited to the identification of the lane
change execution phase, so the definitions 5
addressing the preparation phase are not included in the review.
Worrall and Bullen conducted a study of 6
lane changing behavior at a macroscopic level on multilane
highways (4). Lane change pattern (the 7
number of lane changes occurring among all lanes along a given
length of road and over a given time 8
span), frequency (the distribution of lane changes between
specific lane-lane pairs along a given road 9
length and over a given time span), maneuver length
distributions, maneuver time distributions, and gap 10
acceptance behavior were described. Data were collected using
70mm aerial photography taken at 11
different locations. Lane changes were divided into head, lane-
change, and tail stages. The head portion 12
refers to the period of time between the moment when the
vehicle moves from a straight path and that 13
when it first encroaches the lane line separating the current and

the target lanes. The lane change stage 14
follows the head portion and ends when the vehicle body fully
crosses the lane line. This is followed by 15
the tail portion which ends when the vehicle resumes a straight
path. 16
Chovan et al. addressed the definition of lane change in an
analysis of lane-change crashes 17
guiding the development of a crash avoidance system. In the
report, lane change refers to a family of 18
maneuvers including simple lane change, merge, exit, pass and
weave maneuvers. Lane change was 19
defined as a deliberate and substantial shift in lateral position of
a vehicle (5). This definition explicitly 20
excludes unintended drift either within the lane or across lanes.
The definition is followed by a model of 21
ideal lane change behavior, partially based on the work of
McKnight et al. (12). It comprises (in order of 22
occurrence) checking the legality of the lane change,
information gathering and decision making, using 23
signal, and execution of the lane change. One might notice that
the model referred is different from the 24
definition used in the same paper since the model includes a
lane change decision phase, which might not 25
involve noticeable lateral movement. 26
Winsum et al. studied the relationship between perceptual
information and motor response during 27
a lane change in a driving simulator (13). Specifically, it
explains the relationship between visual 28
feedback and a driver’s steering actions. Lane changes were
defined using a three-phase method. The first 29
phase begins with initiation of steering wheel movement and
ends when it is turned to the maximum 30
angle from the neutral position. In the second phase, the
steering wheel is turned in the opposite direction. 31

The second phase ends when the steering wheel passes through
the neutral position. At this moment the 32
maximum vehicle heading is reached and the vehicle is at its
largest deviation from longitudinal direction. 33
In the third phase, the steering wheel keeps turning to reach the
second maximum angle (in the opposite 34
direction). This model offers simplicity since steering wheel
angle is the only parameter involved in the 35
identification; however, the down side of only using steering
wheel angle is that this method might not 36
capture lane changes on curved roads since the steering pattern
would be affected by following the 37
contour of the roadway in addition to completing the lane-
change maneuver. Also, when the third phase 38
ends, the vehicle has not yet stabilized in the target lane. At the
moment the third phase ends, the vehicle 39
heading is on its way back to following the direction of the lane
from its maximum deviation. 40
Olsen et al. have closely examined lane changes and provided
multiple criteria for lane change 41
initiation and end points (8, 14). In the dissertation, the author
addressed three issues: 1. To characterize 42
slow lead vehicle lane change; 2. To develop a predictive model
of lane changing; 3. To provide design 43
guidelines for lane change collision warning systems. Driving
data were collected using two 44
instrumented vehicles (a sedan and a SUV). The lane change
initiation point was identified using one or 45
more of the following four rules: 46
1. Vehicle begins to move laterally relative to the lane; 47
2. Driver initiates a steering input intended to change the
direction of the vehicle relative to the 48
lane; 49

3. Driver returns gaze to the forward view after looking in
mirrors or looking directly toward the 50
side or rear; 51
4. Vehicle leaves the lane at least temporarily. 1
In addition, activation of the turn signal is referenced as an
auxiliary criterion. According to the 2
author, the turn signal could be used to locate a lane change, but
cannot be relied upon as the initiation 3
point since the turn signal activation is not present in all lane
changes and does not always represent the 4
initiation point of the maneuver. The completion point,
according to the author, was not as critical as the 5
initiation point. However, it affects task completion time. A
lateral-velocity-threshold method for 6
completion point identification was suggested by the author,
though in practice the end point was 7
determined by data reductionists’ judgements with regard to
‘settling in the lane’. The author did not 8
address whether the velocity threshold method was consistent
with reductionists’ judgements. 9
Tijerina et al. studied eye glance behavior using instrumented
vehicles(6). In this work, the 10
authors provided an understanding of the drivers’ glance to the
road ahead, mirror use, and head rotation 11
during the lane change preparation phase. The study aimed to
provide design guidelines for lane change 12
collision avoidance systems. Lane change maneuvers were

defined as separate decision and execution 13
phases. The decision phase was defined as the time interval
from when the driver desires to change lanes 14
until the initiation of the execution phase by steering input.
This duration was used by the driver to gather 15
information for deciding whether or not to change lanes. The
execution phase is defined as the interval 16
from the initial steering wheel input until the vehicle is
stabilized within the target lane, returns to the 17
original lane, or a crash occurs. 18
Salvucci et al. introduced a real-time system used to predict the
occurrence of lane changes (15). 19
The system is able to continuously infer driver’s unobserved
lane change intentions from observed 20
behaviors. Data were collected from both a driving simulator
and an instrumented vehicle. In this work, 21
Salvucci et al. defined lane change as a segment in which the
vehicle starts moving toward another lane 22
and continues, without reversal, through to that lane. By saying
‘without reversal’, the definition 23
emphasizes the completion of the lane change maneuver and
excludes aborted maneuvers. In order to 24
differentiate real lane changes from unintended drifts and to
define the initiation of lane changes, a 25
minimum threshold of lateral velocity was used. The lane
change initiation point is defined as the moment 26
when the vehicle lateral velocity reaches the threshold. The
threshold was set to be 0.35m/s which, 27
according to the author, is conservative because a lane change
maneuver would take 10s to finish at 0.35 28
m/s (assuming lane width is 3.5m) while the range of mean
values of lane change duration is from 3 to 7 29
seconds (14). Although using 0.35 m/s is based on existing
observations of lane-change duration, it is 30
clear that the appropriate lateral velocity threshold may vary

with driving context since many factors 31
appear to influence lane change duration including road
conditions(city road or highway) (6), the 32
presence of a ride-along experimenter (7), and vehicle types
(16). 33
Toledo et al. presented a lane change definition to address the
influence of the lane change 34
execution phase in the domain of microscopic traffic simulation
where lane changes are conventionally 35
modeled as instantaneous events in such an environment (3).
This study used naturalistic driving data 36
collected by high-mounted video cameras. Lane change is
defined as passing from one lane to the lane 37
immediately next to it. The initiation and completion point are
time instances when the subject vehicle 38
begins and ends lateral movement. The authors were also trying
to associate lane change durations with 39
various factors including lane change directions, vehicle types
and surrounding traffic. 40
When Fitch et al. examined driver’s behavior leading to lane
change crashes or near-crashes, lane 41
change was defined as a driving maneuver that moves a vehicle
from one lane to another where both lanes 42
have the same direction of travel (17). Data were collected from
naturalistic driving using instrumented 43
vehicles. The report did not take the lateral motion onto the
shoulder of the road or into an oncoming lane 44
into account. Initiation and completion points are described
which were adapted from a study by Lee et al. 45
(8). Three criteria are presented to determine the initiation point
of the lane change maneuver. The 46
predominant criterion is when the driver initiates a steering
input intended to change the direction of the 47
vehicle relative to the lane. This criterion is supplemented by

the second one to accommodate situations 48
when: 1. In-vehicle video is not available; 2. In-vehicle image
contrast is low (e.g. night); 3. Lane change 49
occurs on a curved road. The second criterion of initiation point
is when the vehicle begins to move 50
laterally relative to the lane. The third criterion takes drivers’
visual search into account. It is when the 51
driver returns gaze to the forward view after glancing at a rear-
view mirror or side window. The lane 1
change completion point is defined simply as the time when the
vehicle normalizes in the adjacent lane. 2
In operation, one analyst is involved in determining the
initiation and completion point. 3
Table 1 is a summary of the lane change definitions discussed
above. As described above, 4
creating a standard definition of lane-change maneuvers is
beyond the scope of this investigation. Instead, 5
lane change definitions were classified using the following
criteria. 6
1. Explicit initiation and completion points: In general there
appear to be two types of lane 7
change definition statements. One is a general statement without
explicit information about the 8
duration of the lane change. In other lane change definitions, an
initiation point and a 9
completion point are clearly defined. The column ‘Explicit
initiation and completion points’ 10
specifies which definitions explicitly define the initiation and

end points. 11
2. Data source: Each definition presented is dependent on or
related to a specific source of data. 12
Data sources used in the reviewed papers include: driving
simulators, instrumented vehicles, 13
traffic simulation and naturalistic traffic recording. The source
of data is an important aspect of 14
the definition of a lane change maneuver because certain inputs
are only available in specific 15
contexts (e.g. overhead video data will not be available from an
instrumented vehicle). 16
3. Required parameters: In order to use the definitions described
here to develop an identification 17
method, one must have access to certain information. The
parameters that must be available to 18
utilize these definitions effectively are described; however,
some authors did not explicitly list 19
which variables were collected and utilized. As such, the
variables listed in the following table 20
are derived from the definitions provided in the literature, but
may not be entirely consistent 21
with the variables actually used by the original authors. 22
23
TABLE 1: Characteristics of Lane Change Definitions
1

Explicit
Initiation
and
Completion
Points
Required Parameters Data source
Worrall and
Bullen(4) Yes
heading angle, vehicle dimension,
vehicle lateral position, lane
position,
Naturalistic traffic video
John D. Chovan et
al.(5) No Vehicle lateral position
Existing data
(Crashworthness Data System
and General Estimates
System)
W. van Winsum et
al.(13) Yes Steering wheeel angle Driving simulator
Olsen et al.(8, 14) Yes
Vehicle position/lateral velocity

relative to the lane, steering wheel
angle, driver’s vision, directional
signals
Instrumented vehicles
Tijerina et al.(6) Yes
vehicle lane position; steering
wheel position, travel speed, turn
signal activation, lateral
acceleration; driver eye glance and
head turns.
Instrumented vehicles
Salvucci et al.(15) No Vehicle lateral velocity Driving
simulator, instrumented vehicle
Tomer Toledo et
al.(3) Yes Lateral velocity/position, Naturalistic traffic video
Fitch et al.(17) Yes Steering angle, vehicle lateral position,
driver’s eye glance Instrumented vehicle
2
LANE CHANGE IDENTIFICATION METHODS 3
There are not as many articles that specifically address
developing and implementing computational 4
algorithms to automatically identify lane change maneuvers
from time-series data. Generally, the 5
identification process was an intermediate step as part of lane

change related research. In this section, the 6
objective of each work will be summarized, and important
issues such as the implementation process and 7
required parameters will be discussed in order to support
decisions regarding the use of the method in 8
specific contexts. The focus of this review is to aid researchers
as they make decisions regarding 9
appropriate identification methods for lane change maneuvers.
As such, the implementation process and 10
required parameters presented here are intended only to afford
an understanding of the method that will 11
aid in determining whether an identification method is
appropriate for a given context, and researchers 12
should refer to the original works for further implementation
details. It is also worth noting that not all 13
lane change identification efforts involved implementing data
processing algorithms to identify lane 14
changes. There are many cases when lane change initiation and
completion points were defined 15
subjectively. This can be done by drivers’ or ride-along
experimenters’ noting the initiation and 16
completion points of a lane change maneuver or by having data
reductionists review the time history data. 17
The subjective methods will also be reviewed at the end of this
section. 18
Bogard and Fancher explained how ACC (Adaptive Cruise
Control) influences driving behavior 1
when a lead vehicle changes speed or when a driver decides to
change lanes in a report of the FOCAS 2
(Fostering Development, Evaluation and Deployment of

Forward Crash Avoidance System) program (18). 3
As part of this program, two lane change identification methods
were introduced using GPS data and 4
path-curvature data respectively. The former was briefly
discussed and the latter was elaborated and 5
finally used to identify lane changes. 6
GPS data method: Heading angle was one of the five variables
recorded by GPS at 2Hz. From the 7
diagram of heading angle vs. time, one can easily see two types
of heading angle changes. Smooth 8
changes are due to road curvatures and sharp changes are due to
lane changes. However, low sampling 9
frequency and low reliability of GPS recording prevented this
from being the primary lane change 10
identification method used. 11
Path-curvature data method: This six-step method can be
summarized as: Calculating heading 12
angle and yaw acceleration from path-curvature data;
Identifying heading corners (if the absolute yaw 13
acceleration exceeds 0.01 deg/s2 for more than 5s, the mid-
points of the zero-crossing time are defined as 14
the heading corners); Fitting a reference line between heading
corners and calculating the difference 15
between the heading angle peak and the reference and the area
underneath the pulse. If both values exceed 16
defined thresholds, a lane change maneuver is identified. The
authors provide a full illustration of how 17
this methodology was applied and how the criteria were
developed. The drawbacks are also discussed. 18
This algorithm is based on straight and constant radius road
segments. The assumptions upon which the 19
algorithm is based are not fulfilled for many rural roadways. In
addition, the algorithm only captures lane 20
changes that occur at velocities above 50 mph, and it will not

capture lane changes when a driver enters or 21
leaves a curve during the lane-change maneuver. 22
Based on the hypothesis that a lane change will generate a
noisy-sine-wave-like yaw rate signal, 23
Miller and Srinivasan proposed a method to determine a lane
change maneuver of heavy trucks based on 24
yaw rate (19). This is one of the few articles which focuses
specifically on lane change identification. The 25
method consists of four steps: 1. Bias and noise removal; 2.
Sine wave first half cycle determination; 3. 26
Total time span of a lane change determination; 4. Check. Step
1 aims to make the yaw rate sinusoidal 27
signal center around zero and to eliminate ambient noise by
setting all data points with a yaw rate of less 28
than 0.05 deg/s to be zero. Step 2 is to examine if the yaw rate
signals approximate a sine wave. Step 3 is 29
to find the third zero-crossing point which concludes a complete
lane change. Step 4 is to check if the 30
amplitudes of two half cycles are of opposite signs and
determine whether the identified period represents 31
a “wandering in the lane”. This algorighm results in four
decisions: no lane change, left lane change, right 32
lane change and wander in the lane. According to the author, the
model has a detection reliability rate of 33
80% based on 105 video samples. 34
Thiemann et al. proposed a smoothing algorithm for NGSIM
trajectory data and investigated 35
lane change dynamics (20). The data were obtained from
naturalistic traffic recording. Four situations 36
were filtered out at the beginging of data processing: 1. Lane
changes that were too close to each other 37
(using 5s as seperation threshold) ; 2. Lane changes involving
on- or off-ramps (only using lane changes 38
on the four left-most lanes); 3. Aborted lane changes; 4.

Misjudged lane changes by tracking algorithm. 39
The proposed algorithm addresses the well-defined part of lane
changes -- the time span when the vehicle 40
body ‘rides’ on the lane boundary, which is also the lower
bound of the lane change duration. One of the 41
most important variables used is the lane index that the vehicle
is currently occupying. A certain lane is 42
being used if the mid-point of vehicle front-bumper lies in the
lane. If lane index is found to change 43
between two consecutive timepoints, a lane change event can be
assumed. Having the vehicle dimension 44
available(width especially), the timepoints when the subject
vehicle encroaches the lane line and when it 45
leaves the line were found around the lane change event time.
The modal value of lane change duration 46
obtained using this method is approximately 3s and the authors
suggest that it might take 5 to 6 seconds if 47
preparation and post-processing phases are included. 48
Knoop et al. analyzed the number of lane changes as a function
of the characteristics of the origin 49
and target lane. Their lane change identification method used
loop detectors placed on each lane of a 50
three-lane freeway about 100 meters apart (21). Since time, lane
index, vehicle speed and vehicle length 51
were recorded, a vehicle can be re-identified from one detector
to the next. Therefore if a vehicle was re-1
identified at a downstream detector on another lane, a lane
change was identified. This method is based 2
on the assumption that no driver makes a complete lane change

within 100 meters. According to the 3
author, there are two drawbacks associated with this method:
firstly this method only works in 4
uncongested traffic conditions (vehicle speed greater than 72
km/h); secondly this method does not give 5
the accurate trajectory. 6
Koziol et al. (22), when trying to evaluate an Intelligent Cruise
Control System, proposed a lane 7
change (referred to as ‘Lane Movement’) identification method
using degree of curvature data. First, a 8
time window of 8 seconds was used to examine the captured
data points at each time step (using a 1 9
second step length). Next, the captured data points were
normalized and integrated to find the inflection 10
point. If the point was found, a potential lane change was noted.
Then five parameters characterizing a 11
lane change were computed and compared with their boundary
values to further identify a lane change. 12
These parameters included: the inflection of the degree of
curvature curve; the maximum and minimum 13
values on the degree of curvature curve; the duration between
the maximum and minimum degree of 14
curvature; the duration of the entire lane change. A model
validation was also performed and yielded an 15
identification rate of 0.78 and a false alarm rate of 0.2. 16
Based on the work of Bogard (18) and Koziol (22), Ayres et al.
(23) came up with a vehicle 17
movement identification method to analyze field operational
test (FOT) data. This method is able to 18
detect lane changes, turns and curves on different road types
using yaw rate and velocity. First, sensor 19
data bias and noise were removed. Then time intervals for
potential events (lane changes, turns or curves) 20
were identified using yaw-rate. For each time interval, the

heading angle ratio and the lateral position 21
change were calculated and two consecutive time intervals were
grouped if their yaw-rate peaks were of 22
opposite signs. Finally the calculated heading angle ratio and
the lateral position change were compared 23
with their thresholds to identify lane changes. In addition to the
implementation, the authors explained 24
how the thresholds were set, described the algorithm
performance and discussed potential ways to 25
improve the algorithm. According to the validation study, the
algorithm had an identification rate of 69%. 26
It was able to identify lane changes on a curve but as two
separate events. 27
Xuan and Coifman proposed a lane change detection method
using vehicles trajectory 28
information obtained from DGPS (Differential Global
Positioning System) (24). To begin with, a 29
reference trajectory needs to be established to represent the road
geometry. If road geometry information 30
(center line position) is readily available through methods such
as GIS (Geographic Information System), 31
one can skip identifying the reference trajectory and find lane
change maneuvers by comparing a single 32
trajectory with the existing road geometry. If the source of a
reference trajectory is not available, the 33
reference trajectory will be established using the median of all
trajectories. First, a curvilinear coordinate 34
system needs to be set up using an arbitrary trajectory captured
by DGPS. Then all the other trajectories 35
are resampled and mapped onto the coordinate system. The
median of the lateral distance of all 36
trajectories at each point is defined as the reference trajectory.
After the reference trajectory is established, 37
two types of lane changes were defined and targeted: mandatory
lane change (MLC) and discretionary 38

ane change (DLC). MLCs are found by comparing the reference
trajectory with the mean of all candidate 39
trajectories. The mean of lateral positions with respect to the
reference trajectory during a single lane 40
change exhibits a sinusoidal pattern This becomes the indicator
of the occurrence of a lane change. After 41
correcting for the fact that reference trajectory changes lane,
all the trajectories will become relative to 42
the real road and exhibit the normal lane-changing pattern. Then
a lateral velocity of 0.3m/s was used as a 43
threshold to identify lane change maneuvers and identify the
initiation and completion points of a MLC. 44
In contrast, DLCs are detected based on overtaking maneuvers.
Each overtaking maneuver 45
contains two lane change maneuvers. The lane boundary curves
were set up as threshold curves which are 46
1.8m from the lane center on both sides. If the vehicle is beyond
the thresholds for a certain time and 47
distance period, a candidate overtaking maneuver could be
identified. After eliminating the erroneous 48
identifications due to GPS errors, the real overtaking maneuvers
are found.Then the same lateral-velocity 49
technique used to identify MLCs is applied to find DLCs from
overtaking maneuvers. It is also stated that 50
the lateral-velocity criterion is subject to change during
congestion. 51
As mentioned above, lane change identification work can also
be done using subjective methods 1
based on researchers’ needs. In Salvucci et al.’s study of

driver’s control and eye movement during lane 2
changes(9), a semantic method was used to identify a lane
change maneuver. In a driving simulator, a 3
multi-lane highway environment was simulated and participants
were instructed to report the intentions 4
and completions of lane changes. In cases where a participant
failed to report, an experimenter would 5
define the lane change based on when the initiation and
completion points seemed apparent. A similar 6
work was done by Hanowski when studying driver fatigue using
instrumented vehicles(25). As an 7
auxiliary method to identify critical incidents in a database, the
driver was instructed to use an incident 8
pushbutton after the incident had just occured and then had data
analysts review the time period around 9
the incident location. In both methods drivers were aware of
data collection process, but the authors 10
suggest that these methods were effective. These methods are
presented as a reminder that it may be 11
appropriate to manually identify lane changes from recorded
data. 12
Table 2 summarizes the characteristics of the lane change
identification methods described above. 13
The required parameters are listed because successful
application of lane change identification method is 14
dependent on the fact that all required variables are easily and
accurately available. For subjective 15
methods, required parameters refer to the subjective action
needed to identify lane changes. 16
17
TABLE 2 Lane Change Identification Methods Characterization
18
Required Parameters Data source

Objective Methods
Bogard and Fancher(18)
Method using GPS: Heading
angle; Method using path-
curvature: Path-curvature, velocity
Instrumented vehicle
Miller and Srinivasan (19) Yaw rate Instrumented vehicle
Thiemann(20) Vehicle dimension; lane index; vehicle position
Naturalistic traffic recording
Knoop(21) Vehicle passing time; lane index; vehicle speed;
vehicle length Naturalistic traffic recording
Koziol(22) Degree of curvature Naturalistic traffic recording
Ayres(23) Yaw rate; Velocity Naturalistic traffic recording
Xuan and Coifman(24) Vehicle lateral position Instrumented
vehicle
Subjective Methods
Salvucci(9) Verbal protocol data/experimenter’s judgement
Driving simulator
Hanowski(25) Driver’s activation Instrumented vehicle
19
DISCUSSION AND CONCLUSION 20
This literature review could serve as both a inventory of
relevant efforts and a selection guide for those 21
who need to define and identify lane change maneuvers. This
work also represents a starting point for 22

developing a standard definition of lane change maneuvers and
a set of methods to enable researchers to 23
implement consistent methods for lane change identification. 24
It can be seen from the lane change definition section that the
driver’s steering input and the 25
vehicle lateral movement are frequently used to define the lane
change maneuvers (execution phase). In 26
addition to these triggers, the vehicle heading angle is also
used. In this sense, the lane change execution 27
phases in different definitions can be mapped to similar
durations. However, the lane change preparation 28
phase has not been as rigorously defined. Since the lane change
definition is the foundation of the lane 29
change identification, a good lane change definition should use
variables that can be easily and accurately 30
obtained. Lane changes can be challenging maneuvers, therefore
they may be relevant indicators of driver 31
performance; however, in order to address performance metrics
associated with lane change maneuvers, it 1
is important to use an appropriate definition of the lane change
maneuver that is capable of identifying the 2
time-course of the maneuver. The review presented here could
be utilized to guide researchers in this and 3
other contexts with respect to utilizing an appropriate lane
change definition and identification method. A 4
specific example application is to use lane change maneuvers to
assess driver’s performance in a clinical 5
setting. This context has unique requirements for lane change
related analyses including: 1. Data are 6

usually obtained from driving simulators or instrumented
vehicles; 2. Expensive head position or eye 7
tracking devices are not common in such an environment.
Therefore, participants’ head/eye position data 8
are assumed to be unavailable; 3. With similar performance, the
identification algorithm should be as easy 9
to implement as possible to reduce the workload of data
reductionists; 4. Identification algorithm does not 10
necessarily have to capture the lane change preparation phase,
but should capture durations between 11
vehicle’s first lateral movement and its stabilization in the
target lane. By filtering lane change 12
identification models using the above criteria, Miller and
Srinivasan’s method (19) might be selected for 13
use in the context of driver assessment in the clinical
environment. 14
Even though a significant research effort has been devoted to
defining and identifying lane 15
changes, there is still significant work to be completed before
providing an integrated lane change 16
definition and identification method that will allow for
consistency across research studies. Future works 17
include: 1. establishing a systematic definition of lane changes.
The definition should explicitly define the 18
duration of both lane change preparation phase and execution
phase. It should also take into account of 19
following features: a. lane change direction (right lane change
or left lane change); b. mandatory lane 20
changes and discretionary lane changes; c. handling of multiple-
lane changes (crossing multiple lanes at 21
one time); d. handling of curved roadway segments; 2.
developing a corresponding lane change 22
identification method. The identification method should be able
to identify the phases and lane-change 23
components described in the definition. Additionally, the

required parameters should be easily and 24
accurately obtainable in the contexts for which the lane change
definition is applicable. 25
26
REFERENCES 27
1. Wang, Jing-Shiarn, and Knipling R. R. Lane Change/Merge
Crashes Problem Size Assessment and 28
Statistical Description. Publication DTNH22-91-C-03121. U.S.
Department of Transportation, 1994 29
2. Sen, B., Smith J. D., and Najm W. G. Analysis of lane
change crashes. Publication DOT-VNTSC-30
NHTSA-02-03. U.S. Department of Transportation, 2003 31
3. Toledo, T., and Zohar D. Modeling duration of lane changes.
Transportation Research Record: 32
Journal of the Transportation Research Board, No. 1999,
Transportation Research Board of the 33
National Academies, Washington, D.C., 2007, pp. 71-78. 34
4. Worrall, R., and Bullen A. An empirical analysis of lane
changing on multilane highways. Highway 35
Research Record, Vol., No. 303, 1970, pp. 30-43. 36
5. Chovan, J. D., Tijerina L., Alexander G., and Hendricks D.
L. Examination of lane change crashes 37
and potential IVHS countermeasures. Publication DOT-VNTSC-
NHTSA-93-2, U.S. Department of 38
Transportation, 1994 39
6. Tijerina, L., Garrott W. R., Stoltzfus D., and Parmer E. Eye
glance behavior of van and passenger car 40
drivers during lane change decision phase. Transportation
Research Record: Journal of the 41
Transportation Research Board, No. 1937, Transportation

Research Board of the National 42
Academies, Washington, D.C., 2005, pp. 37-43. 43
7. Hetrick, S. Examination of driver lane change behavior and
the potential effectiveness of warning 44
onset rules for lane change or “side” crash avoidance systems:
Virginia Polytechnic; 1997. 45
8. Lee, S. E., Olsen E. C. B., and Wierwille W. W. A
comprehensive examination of naturalistic lane 46
changes. Publication DTNH22-00-C-07007, U.S. Department of
Transportation, 2004 47
9. Salvucci, D. D., and Liu A. The time course of a lane
change: Driver control and eye-movement 48
behavior. Transportation Research Part F: Traffic Psychology
and Behaviour, Vol. 5, No. 2, 2002, pp. 49
123-132. 50
10. Olsen, E. C. B., Lee S. E., Wierwille W. W., and Goodman
M. J. Analysis of distribution, frequency, 1
and duration of naturalistic lane changes. Proceedings of the
Human Factors and Ergonomics Society 2
Annual Meeting, 2002, pp. 1789-1793. 3
11. Savino, M. R. Standardized names and definitions for
driving performance measures: Tufts 4
University; 2009. 5
12. McKnight, A. J., and Adams B. B. Driver education task
analysis. Volume I: task descriptions. Final 6

report. 1970. 7
13. Van Winsum, W., De Waard D., and Brookhuis K. Lane
change manoeuvres and safety margins. 8
Transportation Research Part F: Traffic Psychology and
Behaviour, Vol. 2, No. 3, 1999, pp. 139-149. 9
14. Olsen, E. C. B. Modeling slow lead vehicle lane changing:
Virginia Polytechnic Institute and State 10
University; 2003. 11
15. Salvucci, D. D., Mandalia H. M., Kuge N., and Yamamura
T. Lane-change detection using a 12
computational driver model. Human Factors: The Journal of the
Human Factors and Ergonomics 13
Society, Vol. 49, No. 3, 2007, pp. 532-542. 14
16. Hanowski, R. J., Wierwille W. W., Garness S. A., Dingus
T. A., Knipling R. R., and Carroll R. J. A 15
Field Evaluation of Safety Issues in Local/short Haul Trucking.
Proceedings of the Human Factors 16
and Ergonomics Society Annual Meeting, Vol. 44, No. 20,
2000, pp. 3-365-363-368. 17
17. G.M.Fitch, S.E.Lee, S.Klauer, J.Hankey, J.Sudweeks, and
T.Dingus. Analysis of Lane Change 18
Crashes and Near-Crashes. Publication DTNH22-00-C-07007
U.S. Department of Transportation, 19
2009 20
18. Bogard, S., and Fancher P. Analysis of data on speed-
change and lane-change behavior in manual 21
and ACC driving. Publication DTNH22-94-Y-47016 U.S.
Department of Transportation, 1999 22
19. Miller, R. J., and Srinivasan G. Determination of Lane

Change Maneuvers Using Naturalistic Driving 23
Data. 19th International Technical Conference on the Enhanced
Safety of Vehicles, 2005. 24
20. Thiemann, C., Treiber M., and Kesting A. Estimating
acceleration and lane-changing dynamics from 25
next generation simulation trajectory data. Transportation
Research Record: Journal of the 26
Transportation Research Board, No. 2088, Transportation
Research Board of the National 27
Academies, Washington, D.C., 2008, pp. 90-101. 28
21. Knoop, V. L., Hoogendoorn S., Buisson C., and Shiomi Y.
Quantifying the number of lane changes in 29
traffic: Empirical analysis. Transportation Research Board 91st
Annual Meeting; 2012; Washington 30
DC; 2012. p. 18. 31
22. Koziol, J., Inman V., Carter M., Hitz J., Najm W., Chen S.,
Lam A., Penic M., Jensen M., Baker M., 32
Robinson M., and Goodspeed C. Evaluation of the Intelligent
Cruise Control System Volume II – 33
Appendices. Publication, 1999 34
23. Ayres, G., Wilson B., and Le-Blanc J. Method for
identifying vehicle movements for analysis of field 35
operational test data. Transportation Research Record: Journal
of the Transportation Research Board, 36
Vol. 1886, No. -1, 2004, pp. 92-100. 37
24. Xuan, Y., and Coifman B. Lane change maneuver detection
from probe vehicle DGPS data. IEEE 38
Intelligent Transportation Systems Conference; Toronto,
Ontario, Canada; 2006. pp. 624-629. 39
25. Hanowski, R. J. The impact of local/short haul operations

on driver fatigue. Blacksburg, VA: 40
Virginia Polytechnic Institute and State University, 2000. 41
ABSTRACTINTRODUCTIONLANE CHANGE
DEFINITIONSLANE CHANGE IDENTIFICATION
METHODSDISCUSSION AND CONCLUSIONREFERENCES
Select an organization you are familiar with as the basis of the
paper.
Risk involves uncertainty, the lack of knowledge of future
events, and the measures of profitability and consequences of
not achieving the project goal. Your organization has decided
that to be successful in the global economy it must expand its
supply base into China or another country approved by your
instructor. This has become a strategic project for the
organization.
Write a 1,400- to 1,750-word paper in which you address the
following risk management items for this supplier global
expansion project:
· Describe the objectives and goals, tools and techniques, and
organizational roles and responsibilities for effective risk
management for the project.
· Describe various information sources that may be used by the
project team for risk identification.
· Identify and describe the risk management documentation that
will be required for the project. Examples include RMP and risk
management log or register.
· Explain the role of risk management in the project planning
process.
Create a risk breakdown structure that outlines the
organization's risk categories.
Consider the following categories:
· Project risks
· Business
· Contract relationships with customers and suppliers
· Management
· Political

· Organizational risks
· Project management risks
· Cost estimates
· Schedule estimates
· Communication
· Technical risks
· Production risks
· Manufacturing concerns
· Logistics
· Support risks
· Maintainability
· Warranty
· External risks
· Procurement
· Material availability
· Lead times
· Quality
· Market

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