Drive Quality Analysis of Lane Change Maneuversfor Naturalistic Driving Studies

Ravi Kumar Satzoda1, Pujitha Gunaratne2 and Mohan M. Trivedi1

Abstract— Analysis of naturalistic driving data provides arich set of semantics which can be used to determine the drivingcharacteristics that could lead to crashes and near-crashes. Inthis paper, we introduce “drive quality” analysis as part of thedrive analysis process of naturalistic driving studies (NDSs)that we have previously introduced in [1]. In this first workon drive quality analysis for NDS data reduction, lane changemaneuvers that are reduced from naturalistic driving data arefurther analyzed in a detailed manner in order to characterizethem. Visual data from multiple perspectives and the data fromin-vehicle sensors are used to characterize lane changes basedon both the ego-vehicle kinematics and ego-vehicle surrounddynamics. According to available literature on NDS and datareduction, this is the first work that presents an analysis ofvisual data from multiple perspectives to characterize andextract semantics related to ego-vehicle maneuvers in NDSssuch as SHRP2.

I. INTRODUCTION

Naturalistic driving studies (NDSs) investigate the con-tributing factors that result in crashes or near-crashes usingthe data which is collected from typical day-to-day drivingsessions without artificial features being introduced by con-trolled experimental studies [2]. Unlike on-road predictionsystems like [3], NDSs involve offline data analytics [1], [4].The 100-car study [5] and the more recent Strategic HighwayResearch Program (SHRP2) [2], [6] are examples of suchNDSs. Large amounts of naturalistic driving data is collectedin such studies, which is further reduced into specific eventsand conflicts as defined in the data reductionist dictionary [7].While data reduction is largely a manual process by traineddata reductionists [1], there are increasing number of worksthat are aimed at introducing automation or semi-automationin this process [1], [8].

In our previous works on automating the data reductionprocess in NDSs, we have introduced drive analysis in [1],[9], [10]. In drive analysis, naturalistic driving data frommultiple sensors is fused and analyzed using the proposedtechniques in [1], [9] to detect events that are listed inthe data reductionist dictionary [7], which are considered aspossible causes for crashes or near-crashes. A detailed driveanalysis report is generated for each drive that gives a listof semantics which are related to specific events during thedrive. This includes detection of lane change events [1], lanedrift events [9], overtaking vehicle detection [11] etc.

1 Both Ravi Kumar Satzoda and Mohan M. Trivedi are withLaboratory for Intelligent and Safe Automobiles, University of Cal-ifornia San Diego, La Jolla, CA rsatzoda@eng.ucsd.edu,mtrivedi@ucsd.edu. 2 Pujitha Gunaratne is with Toyota Collab-orative Safety Research Center (CSRC).

Fig. 1. (a) Left lane change is being detected from the forward view ofthe camera. (b) The left lane (the lane to the right side with respect to thereader) in the rear view shows that there is a vehicle in the left lane that istrying to pass the ego-vehicle.

While the detection of such precipitating events is criticalfor data analysis and reduction in NDSs, the events alonemight not describe the complete scenario. For example, aleft lane change event is detected from the forward viewof the ego-vehicle in Fig. 1(a). However, detecting the leftlane change event alone does not describe the quality of thedrive. If the dynamics of the surrounding vehicles are alsoconsidered, then we see that the lane change can be a riskymaneuver because there is a vehicle in the left lane behindthe ego-vehicle (as shown in the rear camera view in Fig.1(b)), which is trying to pass/overtake the ego-vehicle.

In NDS drive analysis, characterizing the maneuvers madeby ego-vehicle as described above adds higher order semanticinformation to the events that are identified in the datareduction process. While our previous works on automatingthe data reduction process through drive analysis focused on“detecting” the events such as lane changes and lane drifts, inthis paper the events are further characterized in automatedmanner to provide more information about the precipitatingevents in the data reduction process of NDSs.

In this paper, we particularly focus on the drive analysisreport that is generated for lane change events during adrive. In [1], lane change event detection was performed forreducing NDS data using the lane detection method proposedin [12], [13], and a drive analysis report was generated todetermine the number and type of lane change events duringa drive. In this paper, drive quality analysis is performed

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on the detected lane change events to characterize them asaggressive or non-aggressive. In order to do this, we analyzethe visual data from two perspectives and analyze the dataabout the ego-vehicle dynamics within the lane along withthe surrounding vehicles that are detected from the multi-perspective views. To the best knowledge of the authorsbased on open literature on NDS, this is the first work thatis analyzing multiple perspectives as stated in NDSs such asSHRP2 [2] to characterize critical events for data reduction.The proposed analysis is applied on real-world NDS drivingdata and a drive quality analysis report is generated at theend.

Fig. 2. Overview of proposed drive quality analysis.

II. OVERVIEW AND SCOPE OF DRIVE QUALITYANALYSIS

In this paper, we present drive quality analysis as an addi-tional information gathering step for data reduction in NDSssuch as SHRP2. Considering that data reduction involves afew hundred different events in NDSs [7], the scope of thispaper is limited to lane change maneuvers of the ego-vehicle.Fig. 2 shows the overview of proposed drive quality analysis.Given the sensor data from multiple sensors such as camerasand in-vehicle sensors that capture the vehicle kinematics,the drive analysis process [1] detects lane change events.The resulting events are further analyzed to generate drivequality analysis reports. The visual data from two camerasis analyzed further for presence of vehicles, and the vehicledetection information is fused with the lane change eventsand vehicle kinematics to generate drive quality analysisreport. In this paper, we limit the study to lane changecharacterization only, and future works will explore drivingstyles from the proposed characterization. The detection oflane change events is discussed in our previous work [1].

Before getting into the details of drive quality analysis,we describe the sensor setup framework that is used forthis analysis. The sensor suite is similar to the DAS sensormodule that is used in the on-going NDS called SHRP2 [2].This involves two cameras that are looking outside (forwardand rear views of the ego-vehicle). Additionally, the vehicle

kinematics such as speed, acceleration, steering angle etc.are captured by the in-vehicle sensors through the vehicleCAN (controller area network) bus. The visual informationfrom the forward and rear facing cameras is used to performthe drive quality analysis, and this is the first time (basedon publicly available literature on NDS and drive analysis)the two camera views are being analyzed for automated dataanalysis of naturalistic driving data.

Additionally, it is to be noted that the proposed work inthis paper is catered for NDS data analysis, wherein thenaturalistic driving data is already collected and is availablefor analysis. Unlike active safety where the detection orprediction should be done either real time or before the actualevent occurs, drive quality analysis is performed on the entiredata after it is collected at the end of the drive. This impliesthat both past and future data from the time of occurrenceof an event can be used for data analysis. Also, we willrefer to published lane and vehicle detection algorithms inthis study, which will be used for characterizing the driveanalysis events. Therefore, the detection algorithms are notin the scope of the paper.

III. CHARACTERIZING LANE CHANGE MANEUVERS INNDS DATA

In this section, lane change maneuvers are characterizedfor NDS data analytics. The lane change maneuver is firstdefined followed by the steps to characterize the lane changemaneuvers.

Fig. 3. Lane change maneuver illustration on timeline.

A. Lane Change Maneuver Timeline

The detection of the lane change event is described in[1]. A lane change is usually detected using either of thefollowing two criteria: (a) when the front wheel (left/right)of the ego-vehicle crosses the lane marking, or (b) when thecenter of the wheel crosses the lane marking. Both the abovecriteria describe the time instance when the lane change eventis detected. However, in order to analyze the lane changemaneuver, we need to define the time window of the lanechange maneuver.

In order to define the time window Tlc = {tmin, tmax} forlane change maneuver, the drift information of the vehicleis combined with the lane change event detection. We willdescribe this using left lane change and the same can beextended for right lane change events also.

Consider the lane change maneuver timeline shown inFig. 3. Before the left lane change begins, the vehicle driftstowards the left lane (denoted in red). After the lane change

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occurs at tlc and before the ego-vehicle centers itself on theleft lane, there is a period of time when the ego-vehicleappears closer to the right lane (denoted by red in theright most window). Therefore, the ego-vehicle appears tobe drifting right before it enters into the lane completelyafter the lane change maneuver.

In order to determine tmin and tmax, the lane drift eventsare also detected while detecting the lane change event usingthe techniques proposed in [11]. tmin and tmax for left andright lane changes are then defined as follows, given thedrift events are detected immediately before and after thelane change maneuver:

tmin = Beginning of left (right) lane drift (1)tlc = Left (right) lane change time instance

tmax = Ending of right (left) lane drift

B. Lane Change Characteristics using Surrounding Vehicles’Dynamics

The dynamics of the surrounding vehicles are first usedto characterize lane change events in NDS. In this sub-section, we will analyze the presence of vehicles in frontand rear of the ego-vehicle to determine the drive qualityduring lane change events. In order to do this, a two-camerasetup as shown in Fig. 4(a) is used. This sensor configurationis similar to the sensor setup in the on-going SHRP2 [2] and100 car study [5]. Therefore, the proposed techniques canbe used to analyze SHRP2 NDS data. As noted previouslyin the introduction, the proposed work in this paper is thefirst instance in the context of NDS wherein the visual datafrom multiple perspectives is being used for automated dataanalytics in NDS.

Fig. 4(b) & (c) show the forward and rear views fromthe ego-vehicle as seen by the two cameras CF and CRrespectively. Henceforth, the image frames at time ti fromthe forward and rear cameras are denoted by IFti and I

Rti

respectively.Assuming that the lane change event occurs in the time

window Tlc (derived in Section III-A), vehicle detectionalgorithms are applied on IFti and I

Rti in the following manner.

Let us assume that a left lane change event is detected. Weconsider the following two vehicle detection algorithms inthis study:

1) Overtaking vehicle detection algorithm [11] to detectthe presence of partially visible passing or recedingvehicles.

2) Active learning based vehicle detection [14] to detectvehicles in the forward and rear views of the ego-vehicle.

It is to be noted that the vehicle detection itself is not themain contribution of the paper; however the use of vehicledetection for NDS data analysis is the focus of the paper.Therefore, other vehicle detection techniques can also beused for the proposed data analysis.

Let us consider the case when there are no passing vehicles(overtaking or receding vehicles). Let SFV and S

RV be the sets

of detection windows of the vehicles in the forward and rear

Fig. 4. (a) Two-camera sensor setup for NDS data analysis similar toSHRP2 NDS, (b) forward view captured by CF , (c) rear view captured byCR.

views IFti and IRti from the ego-vehicle respectively. Given

the lane localization information, which was used to detectthe lane change events, the windows in SFV and S

RV are then

localized within the left lane (considering left lane changeevent). Among the selected windows, the window that is theclosest to the ego-vehicle is denoted by W F and W R for theforward and rear views respectively. In order to determinethe closest window, inverse perspective mapping (IPM) isused to determine the position of the windows in real-worldcoordinates [15], [16]. It is to be noted that this analysis ismeant for NDS analytics. Therefore, camera calibrations arepre-determined and can be used to determine the IPM basedestimation of depth of the vehicles. If there is no vehicle inthe left lane, then W is set to null.

Fig. 5. Illustration showing the distances of the vehicle in the forward andrear views.

Next, the time to distraction Td is used to determine thedistance from the ego-vehicle, which can be considered asthe region where a near-crash or a crash can occur due tothe distraction of the driver. According to several studies [17]on driver distraction Td = 1.5 seconds is considered as the

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maximum allowable distraction time for the driver to avoidany crash or near-crash. Given the speed v and accelerationa of the ego-vehicle from in-vehicle sensors, the distancein front of the ego-vehicle at ti (tmin ≤ ti ≤ tmax), which isconsidered as the region of interest that can pose a threat tothe ego-vehicle due to driver inattention is computed usingthe following equation:

Dti = vtiTd +12

aT 2d (2)

Now, given the windows of nearest vehicles in the left laneof the ego-vehicle (for a left lane change event), i.e., W F

and W R, the distances of the vehicles are determined usingthe inverse perspective transformation [15]. These distancesare denoted by dFti and d

Rti (illustrated in Fig. 5). d

Fti and d

Rti

are used to define the risk posed by the vehicles in the leftlane in front and rear of the ego-vehicle as follows:

ψFti = 1−dFtiDti

ψRti = 1−dRtiDti

(3)

The above formulations for the risk posed by the nearestvehicles will range from 0 (farther away from the ego-vehicle) to 1 (closer to the the ego-vehicle). If no vehicleis found within the Dti distance from the ego-vehicle, therisk factor is set to 0. The above formulations are meant forthe case when there are no passing or overtaking vehicles. Ifsuch vehicles are detected, then ψ is set to 1. Therefore, allthe different cases for the risk can be summarized as follow:

ψFti =

0 if no vehicle within Dti1− dtiDti for d

Fti < Dti

1 overtaking/receding vehicle found(4)

The above formulation is for the risk posed by vehiclesin front of the ego-vehicle. Similarly ψRti is computed forvehicles in rear view of the ego-vehicle. The overall risk attime instance ti is then defined by

ψti = max(ψFti ,ψ

Rti ) (5)

In this study, we are using the maximum function to definethe overall risk. However, other functions such as a weightedaverage could also be used.

The above risk value is computed for all ti ∈ [tmin, tmax],i.e. the lane change event time window. If the mean risk forthe duration, µψ , is greater than a threshold Tψ then the lanechange event is considered to be a risky lane change basedon the dynamics of the surrounding vehicles.

C. Lane Change Characteristics using Ego-vehicle Kinemat-ics

In this sub-section, the ego-vehicle kinematics are used tofurther characterize a lane change event.

1) Steering Angle: Given a lane change event, the steeringangle θti at every time instance tmin≤ ti≤ tmax during the lanechange is used to create a profile function f (θti) for the lanechange event. Fig. 6 shows the two such profiles of a left

Fig. 6. Steering angle profiles for left lane change and right lane change.Notice that the right lane change is shorter in duration but has larger steeringangle deviation as compared to the left lane change.

lane change and right lane change. The steering angle profileis used to determine change of steering angle at every ti, i.e.,

∆(θti) = θti+1 −θti (6)

The steering angle change is then used to determine thefollowing two measures - cumulative absolute steering anglerate and mean cumulative absolute steering angle rate :

Φθ = ∑ti|∆(θti)| and Φθ =

Φθtmax− tmin

(7)

Φθ gives the total change in steering angle, whereas Φθgives the rate of total change in steering angle. Higher valuesfor both Φθ and Φθ usually indicate a sharper lane changeevent. For the lane changes shown in Fig. 6, the values forthe two measures are as follows: (1) Φθ = 8.3, Φθ = 0.23,(2) Φθ = 16.4, Φθ = 0.63. A higher value of Φθ indicatesthat the lane change was performed in lesser amount of timewith larger variation in steering angle. It can be seen fromFig. 6 that the duration of the right lane change is shorterand the steering angle variation is higher as compared to theleft lane change. These values corroborated with the visualinspection that the right lane change is more aggressive andsudden than the left lane change.

Fig. 7. Lateral acceleration for left lane change and right lane change.

2) Lateral Acceleration: We next look at the lateralacceleration of the ego-vehicle during lane change. Lateralacceleration (ay) has been studied in relation with drivingstyles in [18]. In this section, we look at ay in relation tolane changes and events in NDS data reduction. In NDS data

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reduction dictionary, lateral acceleration is used in multipleevents to determine causes for crashes and near-crashes. Forexample steering left or right with ay > 0.25g is consideredas a critical event. Similarly, ay > 0.4g is also an indicationfor near-crash/crash.

The lane changes also include steering left or right. There-fore, the lane changes in NDS data are characterized usingthe lateral acceleration. Fig. 7 shows the profiles of absolutevalues of lateral acceleration during two lane changes. Themean value µay of ay is computed for the segment of the drivewhen a lane change occurs, i.e. between tmin and tmax. Themean value is then used to characterize the lane change asaggressive or non-aggressive. For example, referring to Fig.7, the µay is determined as 0.21g and 0.15g respective for theright and left lane changes. If we set a threshold of Tay = 0.2gto classify a lane change as aggressive or non-aggressive, theright lane change is considered as an aggressive lane change.

IV. DRIVE QUALITY ANALYSIS FOR NDS:VISUALIZATIONS AND REPORT GENERATION

In this section, the drive quality analysis report is gen-erated for naturalistic driving data. First, we consider onelane change event and demonstrate all the different measuresthat were formulated in the previous sections. The visualdata from the two cameras and the vehicle kinematics datafrom the in-vehicle sensors are combined using the proposedformulations in order to generate the different profiles asshown in Fig. 8.

Fig. 8(a) shows the risk posed by the nearest forward andrear vehicles (with respect to the ego-vehicle) in the left laneduring the lane change interval. It can be seen that the riskfrom the rear-vehicle is consistently higher compared to theforward vehicle during the lane change maneuver. However,after the lane change event occurs, the forward vehicle seemsto be posing higher risk. This was corroborated by manualinspection of the visual data, which showed that the ego-vehicle accelerated (longitudinal acceleration) towards theend of the lane change maneuver. Fig. 8(b) shows the overallrisk, which is the maxima function that was described inSection III-B. The overall risk ensures that the risk posedto the ego-vehicle is determined by the nearest vehicle forevery time instant.

Next, the profiles of steering angle and lateral accelerationof the ego-vehicle during the same lane change event arevisualized in Fig. 8(c) & (d). It can be seen from the steeringangle profile in Fig. 8(c) that the lane change event didnot involve sudden steering angle movements because amaximum deviation of about 5◦ occurred during the lanechange event. However, the lateral acceleration profile showsthat the ego-vehicle moved at lateral accelerations over 0.2gfor most of the lane change event.

The profiles in Fig. 8 can used by the data reductionists inNDS as tools to visualize the lane change event. The metricsdescribed in Section III derived from the profiles in Fig. 8are then used to generate reports. First, individual reportsfor the each lane change event are generated. Table I liststhe different measures related to this particular lane change

TABLE ILANE CHANGE EVENT QUALITY ANALYSIS REPORT

Quality metric Threshold Measure AnalysisMean risk µψ 0.4 0.47 AggressiveCumulative steeringangle Φθ

10◦ 3.5◦ Non-aggressive

Mean cumulativesteering angle Φθ

0.4 0.06 Non-aggressive

Mean lateral accelera-tion ay

0.2g 0.22g Aggressive

event, and it also tags the event as either aggressive or non-aggressive. It can be seen from Table I that 2 of the 4 metricscharacterize the lane change event to be aggressive.

Next, all the lane change events in a given drive fromnaturalistic driving data can be characterized to generatedetailed reports related to the quality of drive based on themetrics presented in this paper. Table II shows one suchreport, which one can use to deduce the following. It canbe seen that there are more high risk left lane changes ascompared to right lane changes. In the context of right sidedriving in US, this means that the drive involved more highrisk lane changes towards faster moving traffic. Furthermore,there were 10 lane changes that are tagged as high risk dueto ego-vehicle kinematics. This shows that the driving styleitself could be aggressive and a contributing factor for highrisk lane changes.

TABLE IIQUALITY ANALYSIS REPORT FOR ENTIRE NDS DRIVE

Semantic Analysis ResultNumber of lane change events 35Left lane changes 21Right lane changes 14High risk lane changes (overall) 10High risk left lane changes (overall) 7High risk right lane changes (overall) 3High risk due to traffic 10High risk due to vehicle kinematics 14

V. DISCUSSION, REMARKS & FUTURE DIRECTIONS

This section is deliberately titled as discussion and re-marks, and not as conclusions. This is because we believethat the analysis presented in this paper is only a firststep towards more such studies for automatically analyzingnaturalistic driving data, that could enable a multitude of so-lutions for driver safety, traffic safety systems and controllersfor intelligent vehicles. In this study, we characterized lanechange maneuvers that are detected in naturalistic drivingdata based on two camera views, ego-vehicle dynamicsand in-vehicle kinematics information. Semantics such asthe aggressive nature of the lane change maneuvers areextracted to generate drive quality analysis report. Furtherwork needs to be done to involve more contributing fac-tors to the characterization of the lane change maneuvers,such as the presence of vehicles in blind spots, surroundview information such as traffic signals, traffic lights etc.

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Fig. 8. Sample profiles during a lane change event.

Additionally, considering that lane and vehicle detection arebeing explored for implementation on embedded processors[16], [19], [20], online drive analysis using mobile computingplatforms in order provide the driver an analysis of the drivein real-time is a possible future direction.

ACKNOWLEDGMENTSThe authors would like to acknowledge the sponsors - UC

Discovery Program and associated industry partners, especiallyToyota CSRC, for their support. We thank our colleagues fromLaboratory for Intelligent and Safe Automobiles, UCSD for con-structive comments and support.

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