j ourna l h o mepage: www.elsev ier .com/ locate /aap
dapting ISA system warnings to enhance user acceptance
elipe Jiménez ∗, Yingzhen Liang, Francisco Aparicionstituto Universitario de Investigación del Automóvil (INSIA), Universidad Politécnica de Madrid, Campus Sur UPM, Carretera de Valencia km 7, 28031 Madrid, Spain
r t i c l e i n f o
rticle history:eceived 24 August 2009eceived in revised form 26 January 2010ccepted 25 May 2010
eywords:SAariable speed limits
a b s t r a c t
Inappropriate speed is a major cause of traffic accidents. Different measures have been considered tocontrol traffic speed, and intelligent speed adaptation (ISA) systems are one of the alternatives. Thesesystems know the speed limits and try to improve compliance with them. This paper deals with an infor-mative ISA system that provides the driver with an advance warning before reaching a road section withsingular characteristics that require a lower safe speed than the current speed. In spite of the exten-sive tests performed using ISA systems, few works show how warnings can be adapted to the driver.This paper describes a method to adapt warning parameters (safe speed on curves, zone of influence of
riving parametersser acceptance
a singular stretch, deceleration process and reaction time) to normal driving behavior. The method isbased on a set of tests with and without the ISA system. This adjustment, as well as the analysis of driveracceptance before and after the adaptation and changes in driver behavior (changes in speed and path)resulting from the tested ISA regarding a driver’s normal driving style, is shown in this paper. The mainconclusion is that acceptance by drivers increased significantly after redefining the warning parameters,but the effect of speed homogenization was not reduced.
. Introduction
Inappropriate speed is one of the most frequent concurrent fac-ors in traffic accidents, in addition to distractions. According to theuropean Commission (2003), driving at an inappropriate speeds considered as one of the most important factors responsible foreaths and injuries in traffic accidents. Furthermore, approximatelyne-fifth of all people killed in traffic would have survived if driversad remained within the speed limits (Warner and Aberg, 2008b).owever, given that these limits are generally dismissed (Shinarnd Drory, 1983; Hughes and Cole, 1984; Parker, 1991; Chowduryt al., 1998; Donald, 1998), previous results have revealed thatxceeding the speed limit is not regarded as particularly seriousy road users (Stead et al., 2005; Corbett and Simon, 1999). There-ore, drivers question the rationality of speed limits if they are notet according to objective safety criteria but rather according toolitical needs (Mannering, 2009). Thus, one reason to exceed the
imit is the belief that it is not connected to safety; drivers usuallyink perceptions of safety to the likelihood of being ticketed. In con-rast, it has been proven that speed limits lead to a drop in drivingpeeds and driving speed dispersion, which leads to an objective
mprovement in safety (Utzelmann, 1976). This result is in line withhe conclusions drawn by Solomon (1964), Hauer (1971), Salusjarvi1988), Finch et al. (1994), Maycock et al. (1998), Quimby et al.
(1999) and Kloeden and Mclean (2001), among others, concerningthe likelihood of being involved in an accident.
According to Ajzen’s theory (1985), human behavior is deter-mined by intentions, which, in turn, are conditioned by attitudes,subjective regulations and perceived control, as well as elementsof the immediate surroundings (the road or other users). Summala(1988) has proposed the theory that drivers are motivated to higherrisk behavior to achieve certain benefits (e.g., emotion, less travel-ling time); but, with experience, their task at the wheel becomesan “automatic” activity in which risk control is based on main-taining certain safety margins, and they are unable to make anobjective evaluation of risk at every instant. As a result, restrictiveand coercive measures (like speed limits) are advisable to improveoverall safety because they limit the objective function and leadto a lower accident rate. Kokubun et al. (2005) have suggested thatdrivers tend to avoid risky situations if they perceive them, but thatthe ability to perceive risk can be enhanced. However, at times,poor risk perception can be caused by the road geometry itself,which has been clarified by Mori et al. (1995). That is, the state ofthe infrastructure or the weather can induce drivers to assess therisks involved inadequately. It should also be noted that driverstend to overestimate their skills at the wheel and underestimaterisks (Sundstrom, 2008; Tronsmoen, 2008). Therefore, any systemsthat enhance objective risk perception lead to an improvement in
safety; however, if they are to be effective, drivers must trust them.
According to the theory presented by Gabany et al. (1997), thereare five factors that predispose drivers to speed: ego gratifica-tion, risk taking, time pressure, disdain of driving and inattention.
othengatter and de Bruin (1988) have identified four factors thatxplain how drivers choose their speed: the pleasure of driving,isk, travel cost and travel time. Furthermore, drivers with high-erformance vehicles tend to be less perceptive of the risk involved
n speeding than those who drive cars with low top speeds, but theifference is not significant. In VTT Communities and Infrastructure1998), it has been shown that drivers reduce speed in the pres-nce of pedestrians, a road in bad condition, or adverse weather,lthough drivers are aware that they frequently exceed the lim-ts. Warner and Aberg (2008b) have studied drivers’ beliefs aboutpeeding. Moreover, a study by Corbett (2001) illustrates some dis-repancies between actual speeds and speeds reported by drivers.
Given the negative effects of inappropriate speeds, differenteasures have been considered to encourage drivers to use speeds
hat are appropriate to the prevailing conditions. In this context,n the final report of the MASTER project (VTT Communities andnfrastructure, 1998), three major blocks of measures have beendentified:
Informative and legal measures (e.g., posted and variable speedlimits, vehicle- and driver-specific speed limits, penalty systems,roadside and in-vehicle feedback, and education and advertisingcampaigns)Measures related to road design (e.g., humps and cavities, roadnarrowing and horizontal deflections, roundabouts, rumble stripsand other road surface treatments)Intervening measures (e.g., conventional and automated speedenforcement, adaptive cruise control and in-vehicle variablespeed limiters)
Intelligent speed adaptation (ISA) systems involve a supplemen-ary measure that has already been analyzed by many countriessee literature compilations, such as Várhelyi, 2002b; Aparicio etl., 2005; Jamson et al., 2006). These systems know the speed lim-ts and try to improve compliance with them through warningso the driver and/or interventions in some vehicle systems. Thextent of their development has focused mainly on fixed limit sys-ems. However, different studies have clearly shown the greaterenefit of variable or dynamic limit systems that are dependent onoad geometry and weather conditions (Perret and Stevens, 1996;árhelyi, 1996; Jiménez, 2006; Carsten et al., 1997; Carsten andate, 2005), which is similar to the beneficial use of variable speedimits that depend on the conditions at any particular instant (e.g.,ee et al., 2006). These benefits include reductions in the numberf injury accidents up to 36% (Carsten and Tate, 2005).
Analysis of the effect of advanced driver assistance systemsADAS) on drivers and other road users is critical to exploit theotential benefit of the systems. In the various works that focusn ISA systems, driver behavior has been examined primarily byomparing driving speed with the system either active or inac-ive (comparing informative, voluntary and mandatory settings).spects of user acceptance have also been examined for certainystems. For example, Van Loon and Duynstee (2001) have shownhat using the ISA system leads to a drop in average speed andhat the speed range becomes more homogeneous, which causes
ovement and transformation of the speed distribution, accord-ng to the findings of Carsten et al. (1997). In addition, a groupf drivers has been found to adapt their speed significantly bet-er outside the control zone after using the system. Hjälmdahlnd Várhelyi (2004a) have suggested that the active pedal systemmproves behavior toward other road users. Várhelyi et al. (2004)lso have revealed that test drivers’ compliance with speed limits is
mproved considerably, with a reduction in average speeds and lesspeed variation being observed. Other works, like those of Carstennd Tate (2000) have included simulation modeling works and roadests. Vlassenroot et al. (2007) have identified the high degree of
d Prevention 48 (2012) 37– 48
driver satisfaction with the active gas pedal system. However, theyhave shown that many of the drivers who tested the system tendedto exceed the speed limits because of the behavior of other userswho did not have the system or discrepancies between the roaddesign and the limits set, which is in line with other earlier stud-ies (Brown, 2002). Regarding this system, Hjälmdahl and Várhelyi(2000) have shown that the mental effort required is less than thatof other systems, which means that drivers are more capable ofattending to other traffic issues; however, drivers may feel inse-cure when faced with other users who drive faster. This feelingwill disappear as the system achieves greater market penetration.This conclusion is in line with the results provided by Carsten andTate (2000), who showed that drivers consider themselves vulner-able when using some types of ISA systems because they cannotovertake or accelerate in the event of risk and because they arefollowed by other users at a closer distance, which is accentuatedby other users overtaking them more often than usual. The resultsof system acceptance described by Van Loon and Duynstee (2001)are satisfactory. However, Várhelyi (1999) has shown that driveracceptance increases in adverse conditions with low road-tire fric-tion or low visibility.
Finally, it must be noted that Comte et al. (1997) state that tradi-tional speed control measures, such as traffic calming, have limitedeffectiveness in time and space, as well as the additional draw-backs of lack of public acceptance, secondary costs and possibleaccident migration. This limitation in time can also be seen for ISAsystems when analyzing the long-term effects; over time, the effectof reducing excess speed diminishes as drivers get used to the warn-ings, which makes it easier to ignore them (Warner and Aberg,2008a). Reduced speed is longer lasting in systems with greaterintervention, but these find lower acceptance because most studieshave concluded that informative systems find greater acceptance(e.g., Besseling, 1999; Várhelyi, 1999; Aberg, 2000; Paatalo et al.,2001). Nevertheless, it is essential for drivers to understand andaccept the measures introduced if long-term effectiveness is to beachieved.
In some works, it has been highlighted that, when attempting tointroduce measures to improve traffic safety, such measures mustaddress drivers’ internal motivations if the effects are intended tolast over time (Hatakka et al., 2002; Victoir et al., 2005; Paris and vanden Brocke, 2008). In the case of ADAS, this involves adapting thesystem warnings, actuators and interfaces to the driver, and theireffectiveness depends on user acceptance, which means these sys-tems must adapt to user expectations (Batavia, 1998). Thus, driverscan appreciate the coherence between risk and the system advice. Itmust take into account that the driver response to system warningsdepends on the extent to which they trust them (Bliss and Acton,2003). Similarly, Lee and Kantowitz (1998) have noted that systemutility depends not only on the intelligence of the technology butalso on the joint performance of the system and drivers and thattrust is one of the most important driver cognitive characteristicsfor determining the appropriate use of driver assistance systems.They have stated that the design characteristics of ADAS shouldbe considered from three points of view: driver characteristics,system capabilities and environmental factors. However, adaptingto drivers is not easy; for example, it is difficult to model humanbehavior and variability in driving styles and driving skills. Driv-ing style is influenced by motives, attitudes, personality traits andlifestyle (Erlander et al., 1993; Gregersen and Berg, 1994) and thesestyles can be recognized by analyzing different driving variables.Kuhn and Heidinger (1997) have classified drivers based on accel-eration, braking and cornering behaviors, while Bachmann (1995)
has identified the differences between aggressive or relaxed drivingpatterns.
In fixed limit ISA systems, it is the user interface that undergoesthe greatest adaptation. However, in systems that give advance
ysis an
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arning to drivers concerning a danger zone or situation, the adap-ation must also take other variables into account, such as thenstant when the warning is provided and the deceleration process.omerleau et al. (1999) have described the conditions for a curvearning system (CWS) and have highlighted the need to eliminate
alse alarms and the advisability of considering the relevant charac-eristics of each vehicle and each situation to achieve accurate limitredictions. Likewise, it is thought that the limits imposed shouldot be too restrictive because they would lose credibility with users.oreover, driving too close to the safe limit should not be allowed
ecause of the inevitable lack of precision that might lead to unsafeircumstances. Finally, it is recommended that the system shouldssume a driver reaction time of at least 1.5 s. Thus, to adapt a CWSo different driving styles, Mayser et al. (2003) have proposed threelternatives: relaxed (this style is associated with individual speedsround the 50-percentile), normal (75-percentile), and aggressive85-percentile).
In spite of previous considerations and the extensive tests car-ied out with ISA systems, few works have demonstrated a practicalesign for adapting the warnings of these systems to driving param-ters. This approach is presented in this paper, in addition to howhe system influences user acceptance and its effect on user behav-or. This paper addresses an informative ISA system, described inection 2, that provides the driver with an advance warning beforeeaching a road section with singular characteristics that require
lower safe speed than the current speed. Therefore, apart frometting these limits, the approach circumstances are taken intoccount; all of these factors influence user acceptance. Section 3hows the method and test plan to redefine the system based onarameters measured under normal driving conditions. The redef-
nition is performed in Section 4, and Section 5 includes the resultsf the tests with the new ISA system. Finally, Section 6 includes theain conclusions and future work.
. First generation of the SAGE system
The SAGE system (Aparicio et al., 2003; Jiménez, 2006; Jiménezt al., 2008) is an informative ISA system for rural road envi-onments and has been developed at the University Institute forutomobile Research of the Polytechnic University of Madrid. Thisystem of variable speed limits has been designed according to theremises put forward by Várhelyi (2002a). It comprises the follow-
ng components.
Road geometry: Road signposting usually takes geometric vari-ables, such as curve radius, lane width, superelevation, roadgrade, and extent of visibility into account (e.g., AASHTO, 2001;MOPU, 2000). However, there are still numerous sections of roadwhere speed signposting does not sufficiently reflect reality, eventhough the administrations responsible for the infrastructuretend to correct any anomalies. The system is based on the use of asafe-speed map obtained from a detailed road map that includesthe “safety attributes” defined by the eSafety Forum (2005) andT’Siobbel et al. (2004), such as horizontal and vertical alignment,road grades and superelevations, cross section, visibility distanceand signposting. In addition, a global knowledge of the entireroad allows additional potentially risky situations to be identi-fied, such as the appearance of a bend after a long straight sectionthat encouraged increasing speed, or a bend that is considerablyworse than preceding ones with only a small separation betweenthem.
Vehicle: The system can customize the safe speed limits accord-ing to the type of vehicle and its specific driving characteristicsat every instant. Most countries only consider this aspect forroads with generic speed limits, but not for singular road sections
d Prevention 48 (2012) 37– 48 39
such as bends, crossroads, entrances or exits. This adaptation isachieved by simulating vehicle dynamics with a method similarto that of Easa and Dabbour (2003), where different results areshown for passenger cars and trucks.
• Weather conditions: The SAGE system can also adapt the safespeed limits and warnings to dry and wet conditions by perform-ing vehicle dynamics simulations using the parameters of eachsituation.
With respect to the user interface and driver warnings, the SAGEsystem differs from other ISA systems developed in other countries.To be specific, the purpose of the interface is to warn the driverwhen they are driving too fast when approaching a certain singularroad section or when they exceed legal or safe speed limits and tosuggest the best action that can be taken in order to observe the safespeed limits on dangerous stretches (Jiménez, 2006; Jiménez et al.,2008). The interface contains an indicator that illuminates whenthe driver exceeds the legal speed limit. It also contains a set ofindicators that suggest the required deceleration in order to adaptto a safe speed profile (which can differ from the legal limits) of thesingular stretches of the road. This deceleration a is continuouslycalculated from the vehicle’s position, the current speed v in m/s,the distance to the next singular road stretch d in meters, the safespeed on that stretch vs and the driver’s reaction time tr:
a (m/s2) = v2 − v2s
2(d − trv)(1)
Because greater decelerations are gradually required to adaptthe speed, more indicators are activated and shorter reaction timesare adopted.
The main difference from other user interfaces lies in the factthat, when dealing with informative ISA systems with fixed limits,the driver is only provided with the speed limit information on eachroad section and is alerted if that limit is being exceeded (e.g., VanLoon and Duynstee, 2001), while the SAGE system gives advancewarning, depending on the need to adapt to the speed.
Given the architecture described above, defining the algorithmsinvolves defining a set of configurable parameters that affect thedriving speed set by the system and the greater or lesser proxim-ity to the road section with singular characteristics for which thewarnings are given. These parameters are as follows:
• Criteria for calculating safe speed: As discussed, this calculationis based on the use of a vehicle dynamics simulation model andthe criteria used to set the upper limits for lateral acceleration(Lechner et al., 1983; Felipe and Navin, 1998; Glaser and Aguilera,2003) and tire-road friction (López, 1994). Under normal drivingconditions, it has been verified that the lateral acceleration crite-rion of 3.5 m/s2 is more restrictive than the criterion of combinedfriction.
• Deceleration levels to achieve a safe speed profile: The choiceof these levels, for which the warnings are given, takes safetyand comfort criteria and the recommendations of previous stud-ies into account (Bachmann, 1995; Kuhn and Heidinger, 1997;Soma and Hiramatsu, 1998; Sánchez et al., 2001; Yi et al., 2001).These levels decide the progressiveness of the warnings and varybetween 1 and 5 m/s2.
• Reaction times: The reaction time is assumed to decrease as thewarnings are progressively given while approaching a singularstretch because it is assumed that the driver is now alerted. Inaddition, anomalous warning situations close to these stretches
are resolved when the driving speed is slightly over the limit. Thefigures adopted follow those of previous studies (Olson, 1989;Pomerleau et al., 1999; Yi et al., 2001) and times between 3 and0.6 s are assumed.
40 F. Jiménez et al. / Accident Analysis and Prevention 48 (2012) 37– 48
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Fig. 1. System warnings depending on the required d
Singular road section zone (starting and end points of the limitedspeed zone): As a curved section, the system considers a stretchwhose radius of curvature is less than 1000 m, following the crite-ria used by Imran et al. (2006). However, the restricted safe speedzone only adopts the start zone up to the least favorable point ofthe minimum radius, leaving the driver free to manage the finalpart of the curve.
Fig. 1 shows a diagram of how the warnings are gradually giveno adapt the speed to a vs level at a road section with singularharacteristics.
First, the SAGE system was tested with a sample of eight driverso analyze its performance and the main effects on driver behav-or. The conclusions are described by Jiménez et al. (2008). One ofhe most important results is that, although the average drivingpeed was not significantly reduced, on singular road sections, thepeed of the different drivers is less variable, which is positive from
road safety point of view (e.g., Utzelmann, 1976). Nevertheless,ifferent driver reactions to the system were also observed. Thus,
n a second test stage to study their opinions of the system, a largerample of 30 drivers was used to test the system on the road, on alosed circuit and/or test track. Reichart (1993) states that conflictsetween warnings and driver expectation may lead to a reduction
n driver trust and user acceptance. To evaluate driver perceptionfter testing the system, the drivers were given a short question-aire to describe the adaptation of the warnings and their degreef satisfaction with the system. Table 1 shows the responses to theuestions concerning the adaptation of warning parameters. Therst two questions are closely linked to driving style, particularlyhe lateral acceleration found to be comfortable on curves and theoint at which braking is begun before reaching the curve. In bothases, the largest group considered the warnings to be appropri-te, even though a group of drivers with a more aggressive stylereferred higher limits for taking a curve and for the warnings toe given closer to that road section. The third question is critical,ainly for those drivers who drive in a more relaxed manner and at
ower speed because, according to the warning diagram, the time
etween the first and last warnings is less in these cases. The major-
ty of these drivers perceive the time allowed to be insufficient.his may be justified if the reaction times given in previous studiesake responses to risk situations or situations requiring immediate
able 1river opinions on SAGE system warnings (N = 30 drivers).
High Satisfactory Low
Estimated safe speed on curves 13.3% 60.0% 26.7%Distance from the curve at which
the warning begins40.0% 53.3% 6.7%
Time between the first and lastdeceleration warning
0.0% 46.7% 53.3%
Length of the restricted speed zone 10.0% 80.0% 10.0%
ation level to adapt the speed to the safe speed limit.
action into account, whereas, in this case, the speed adaptation innormal driving does not involve immediate reactions but is a moreprogressive task. Concerning the length of the restricted speedzone, the majority opinion is that it is appropriate.
Finally, only 40% of drivers indicated that they would install thesystem in their vehicles, while 63.3% would consider installation ifsome changes were introduced in the warning system. This resultis reasonable given the information in Table 1. In spite the fact thatSAGE is an informative system, the low acceptance can be justifiedby the fact that the tests were carried out under favorable traf-fic and weather conditions that were not very demanding for thedriver. It should be noted that Várhelyi (1999) has shown clearlythat these systems are more accepted under adverse conditions.Thus, the system was redefined with two basic objectives:
• To maintain its effectiveness with respect to the traffic safety andhomogeneity achieved by the original ISA system.
• To consider more driving parameters so that the system warningsare more naturally accepted by drivers.
Considering that the human–machine interface (HMI) is going tobe maintained in the second generation of the system, the essentialparameters for adjusting the SAGE system are the safe speed lev-els for the singular road sections and the way in which the speedadaptation is recommended up to those levels (which involvesdeceleration levels, singular section influence zone and reactiontimes).
3. Test plan
The redefinition of the system involves a comparison betweendriving with and without the SAGE system and the system param-eters. This study was conducted to adapt the warnings to normaldriving parameters. The method for redefining the SAGE systeminvolved the following stages:
A) Tests with drivers prior to redefining the system:– Free driving to become familiar with the vehicle, the road and
the system.– Driving along the chosen road with the SAGE system inactive
under two conditions: “normal driving” (driving in the usualway) and “fast driving” (trying to arrive in a shorter time tomeet a particular schedule).
– Driving along the chosen road with the first generationSAGE system active (the drivers were asked to follow theinstructions of the warning system in order to compare theirbehavior with and without the system active and to obtain
their opinion about the first generation of the system).
(B) Redefining the SAGE system from the driving data collected inthe previous stage.
(C) Tests with drivers after redefining the system:
F. Jiménez et al. / Accident Analysis and Prevention 48 (2012) 37– 48 41
Table 2Instrumentation and variables.
Source Variables Used by the ISA system Used for driver analysis
InstrumentationCorrevit L-CE-non-contact speed sensor Longitudinal speed X XRMS FES 33 gyroscopic platform Angles drawn about three axes Xa X
Three lineal accelerations XAstech G12 GPS receiver X XSensorisation of pedals Pedal depressed or not XDriver interface XControl unit X X
Other informationDigital map Legal speed X X
Safe speed X XWarning system Legal speed indicator Calculated by ISA system X
Safe speed indicators Calculated by ISA system X
a The ISA system is able to use an inertial measurement system comprising a speed sensor and a gyroscopic platform to locate the vehicle on the electronic map in thee remec
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vent of GPS signal loss. Jiménez et al. (2009) analysed the uncertainty of this measuircumstances without committing any significant errors in positioning.
– Driving along the chosen road with the second generationSAGE system active, evaluating the results, drivers’ opinionsabout the second generation of the system and their accep-tance (the drivers were not given any specific instructions).
All these tests were made with an instrumented vehicle and aample was taken of 10 drivers aged between 28 and 49 who hadeen in possession of a driver’s license for over 2 years.
For the test zone, a single carriageway road was chosen with aomplex geometry of curves of varying severity (16 curves with aafe speed limit below the estimated generic legal speed using therst generation SAGE system) and areas of limited visibility. Thishoice was made based on statistical data showing that the propor-ion of accidents on single carriageway roads due to inappropriatepeed is higher than that for high capacity roads: 23.6% of accidentsn roads of the first type have inappropriate speed as a causal factor,ompared to 18.5% on motorways and dual carriageways, accord-ng to the accidents database of the Spanish Directorate General ofraffic. Deaths in accidents with inappropriate speed account for9.8% of accidents on roads of the first type, compared to 22.3% onoads of the second one.
The vehicle used for the tests was a passenger car fitted withhe SAGE system and additional instrumentation to record driverehavior. As presented by Hjälmdahl and Várhelyi (2004b), in-ar observations can be considered to be a good approximationf real driver behavior if the instruments are hidden and unobtru-ive. This conclusion is counter to that reached by Rathmayer etl. (1999), who considered the influence of external observer in annstrumented vehicle over a long time; thus, the conclusions shoulde carefully analyzed, although the differences are not excessive.
able 2 shows the on-board instruments and the variables used andecorded. The control unit processes the signals to emit warningsnd simultaneously records the warnings and the signals receivedrom the rest of the instruments. All the instruments are hidden
Fig. 2. Percentage of curves on the route where safe speed is exceeded: (a) no
nt and provided estimates of the maximum distance that could be run under these
such that the driver perceives no difference from a standard vehicle,except for the system’s user interface.
4. Redefining the SAGE system
Based on the speed levels on singular road sections, the decel-eration processes and the comparison between driving with andwithout the SAGE system, the system warnings have been rede-fined.
4.1. Speed on road sections with singular characteristics
Of all the curves on the road where the tests were conducted,the ones chosen for the study were those with a safe speed limitcalculated according to the first generation SAGE system that waslower than the generic limit for the road (90 km/h), although norestriction was imposed on the drivers regarding observation ofthe legal limits. Fig. 2 shows the percentage of singular road sec-tions where the safe speed limit set by the ISA system is exceededin the three driving situations with the 10 drivers. Furthermore, itdistinguishes if exceeding the limit occurred throughout the sin-gular stretch or only on one part of it (usually at the beginning). Ascan be seen, the highest excess speed limit percentages occur onthe slowest curves. This fact is more noticeable in normal drivingcircumstances and driving with the ISA system active, even whenthe system considerably reduces this percentage.
There are different speed models for operating speeds on curvesaccording to the radius of the curve, such as those given by Gambard
(1985) or the following expressions for the 85th percentile speed:
v = A2 − B2
R(Lamm et al., 1990; Krammes et al., 1993) (2)
rmal driving, (b) fast driving, and (c) driving with the ISA system active.
42 F. Jiménez et al. / Accident Analysis and Prevention 48 (2012) 37– 48
rable p
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Fig. 3. Operating speed at the most unfavo
v = A3 − B3√R
(Kanellaidis et al., 1990) (3)
here R is the radius of the curve.It should be noted that there are other more complex formulas
hat consider the length of the curve and/or the deflection angle, asroposed by Gibreel et al. (1999) and Voigt and Krammes (1995),nd even consider the lane and shoulder width, radius of the pre-eding curve and the length of the tangent segment (Cardoso, 1995).
However, these expressions are not coherent with the crite-ion of maintaining a certain level of maximum admissible lateralcceleration, which would result in the speed given by Eq. (4):
= A4√
R (4)
This is the criterion used for the first generation SAGE system;ssuming a favorable road surface and weather conditions, as washe case during the tests, the lateral acceleration is a more impor-ant determining factor for calculating the safe speed on curveshan the friction or the braking distance, both of which take onmportance in adverse conditions.
Fig. 3 shows the minimum speed reached on the curves by therivers that drove with extreme (highest or lowest) lateral accel-rations on each curve in the series of tests, as well as the speedsorresponding to the average situation. The parameters of the pre-
ious models were also fitted. The results are shown in Table 3,here it is evident that models (2) and (3) produce a better corre-
ation than model (4). Therefore, for the safe speed calculation, therocedure used for the first generation of the system based on the
able 3arameter estimates for the equations that describe the driving speed (in km/h) onurves according to the radius (in meters).
vehicle’s mathematical model simulation is maintained (Jiménezet al., 2008) maintaining the limits of friction and braking distance;however, the new limit condition derived by applying (3) radius isinserted. This model better reproduces the fact that drivers accepthigher lateral accelerations on curves with a smaller radius than onthose with a larger radius.
It is important to note the high dispersion between the slowestand the fastest test. Two options were considered when imple-menting the SAGE system: (1) offering different levels accordingto each user following the recommendations found in Mayser etal. (2003); (2) taking the average values. Although the former maybe more acceptable to drivers, offering different speed limits forthe singular road sections according to driving style may lessenthe homogenizing effect that the SAGE system was shown to haveon certain sections of road (Jiménez et al., 2008). Thus, the sec-ond possibility was adopted and examined to see if this measurewas favorably received by the drivers involved in the tests (Section5.4).
4.2. Deceleration process
The deceleration process is mainly characterized by the levelof the deceleration and the start and end braking points. Moreover,with SAGE system active, the driver’s reaction time to the warningscan be obtained.
The SAGE system issues warnings assuming uniform decelera-tions. Because there are transitions at the start and end of braking inthe tests, the average deceleration is taken. Fig. 4 shows the accel-erations histogram for the cases of normal and fast driving. Themean values are 0.98 and 1.23 m/s2, respectively and in 96.4% ofcases, the deceleration is below 2.5 m/s2, which may be considereda higher level of comfortable braking.
On the other hand, the reaction time analysis, measured as thetime between the first warning from the system and the driverbeginning to react, follows the distribution shown in Fig. 5. It canbe seen that over 90% of the times recorded fall between 1 and 3.5 s(with a mean value of 2.37 s). However, in 85% of the situations, thedrivers reacted in under 3.2 s.
When driving without the system, it is observed that the timebetween the instant the first warning would be given if the SAGE
system were active and the instant when braking starts, is greaterthan the normal driver reaction time (Fig. 6). This indicates that thewarning is given too far in advance for normal driving without thesystem.
F. Jiménez et al. / Accident Analysis and Prevention 48 (2012) 37– 48 43
Fig. 4. Histogram of mean decelerations: (a
Fig. 5. Distribution of driver reaction time to system warnings.
Fig. 6. Time between the instant at which the SAGE system would give the first warningnormal driving and (b) fast driving.
) normal driving and (b) fast driving.
It should be noted that the first generation SAGE system estab-lishes the restricted speed zone on a curve as the part that fallsbetween the point where the radius of the curve becomes less than1000 m and the point where the minimum radius is reached. Thatis, the warnings are calculated and given to the driver such that, ifthe driver chooses to heed them, the driver can adapt his speed to asafe entry speed. However, an analysis of final braking points whendriving without the warning system active shows that adaptationoccurs inside that zone. Fig. 7 shows distributions with average val-ues of 164 and 145 m, where the 85th percentile is at 220 and 192 mfor normal driving and fast driving, respectively.
It can be seen that the results recorded are coherent withthe responses to the questionnaires in Table 1. Therefore, whenredefining the SAGE system, two opposing situations should beconsidered:
• Drivers circulating at a slightly higher speed than the safe speedfor the next singular section receive deceleration warnings thatare too close together (in the most extreme case, only 2.4 s).According to the data, this time is only enough to react to thewarnings in 57% of the situations.
• Drivers circulating at a significantly higher speed than the safespeed perceive that the warnings are given well in advance ofthe curve. This is related to the zone of influence of the singularsection and the reaction time.
and the instant when braking starts when driving without the system active: (a)
44 F. Jiménez et al. / Accident Analysis and Prevention 48 (2012) 37– 48
at the
w
•
•
•
5S
dt
TA
Fig. 7. Histogram of the value of the radius of curvature of the path
Therefore, taking account the conclusions reached from the testsith drivers, redefining the system involves the following points:
The extreme levels of average deceleration of the second gener-ation SAGE system are set at 0.5–2.5 m/s2, which encompasses82.5% of the cases of driving without the system. It should betaken into account that the change in the lower limit means thatthe warnings are given sooner.In the second generation SAGE system, the reaction time rangehas been modified from 3–0.6 to 3–0 s. Thus, a minimum time of3 s is given until the highest level of warnings is reached (shortertimes were recorded in 83% of cases), but the starting point of thewarnings is not brought forward. Recall that a zero reaction timemeans that the final warning is not given prior to the singularsection, but the driver has indeed received prior warnings withnon-zero reaction times.Finally, the zone of influence of the singular section is redefined,and a curve will be taken as the section of road where the radiusis less than 250 m, given that only 6.3% of the cases analyzed endbraking at a point of larger radius. This change delays the startingpoint of the warnings, as suggested by the drivers in the initialsurvey, and adapts to the driving parameters measured in thetests.
. Results of the tests with the second generation of theAGE system
The second generation of the SAGE system was tested by 10rivers in order to analyze safety-related variables and user accep-ance. The main results are presented in this section.
able 4verage speeds and distance travelled above the legal and/or theoretical safe limits (N = 1
Average speed (km/h) D
Mean Std. deviation M
Compliance with limitsLegal limits 84.3 – 0Legal and safe limits 75.1 – 0
* Differences between this value and the results obtained with the ISA system are stati
point where braking ends: (a) normal driving and (b) fast driving.
5.1. Average speed over the route
The average driving speed in the tests was analyzed first with-out using the ISA system and then with the ISA system. As a globalvariable, the distance travelled over the legal and/or safe limit wasalso analyzed. In addition, using the digital road map, we had thetheoretical average speeds for the whole test route when the driverobserved only the legal limits or observed the legal and safe lim-its; moreover, average accelerations of 1 m/s2 and decelerationsof −1.5 m/s2 were assumed. Table 4 shows the results. It is evi-dent that the safe limits are more restrictive than the legal limits,which reasserts the previously made observation that there are sec-tions of road that are not signposted with appropriate limits. Whencomparing the results of the tests with the ISA system active andinactive, it may be concluded that:
• The average speed with the ISA system is slightly lower(67.1 km/h) than that with normal driving (67.8 km/h), but thedifference is not statistically significant. This result is coherentwith that given by, for instance, Venhovens et al. (1999). However,the standard deviation of the speed is reduced.
• In respect of the distance travelled driving at a speed above thesafe limit, we find that the differences between driving with andwithout the system are statistically significant (21.5% without thesystem and 6.3% with the system), even under normal drivingconditions (15.2%).
• It can be seen that, in general, drivers do not attain average speedsas high as when legal and safe limits were strictly adhered to(except fast driving conditions and with only a slightly signifi-cant difference), even if they drive a considerable distance over
F. Jiménez et al. / Accident Analysis and Prevention 48 (2012) 37– 48 45
Table 5Difference in km/h between the speed at the characteristic points of the road sections with singular characteristics and the theoretical safe speed (10 drivers and 16 curves).
A B C D
Mean Std. deviation Mean Std. deviation Mean Std. deviation Mean Std. deviation
Fast driving 15.2* 11.5* 11.8* 10.0* 6.0* 9.2* 10.4* 10.0*
Total 9.7 11.0 6.7* 11.0* 1.0* 9.9* 4.7* 10.5*
8
e stati
5
so
(
crdcsmdmbtt
bestitb
5
tputAmtta3
odc
made to see if the path appropriately reproduces the way theroad unfolds throughout the lengths of the transition and cir-cular curve sections. Fig. 9 shows an example of an anomalousevolution.
With ISA (second generation) 6.2 9.8 −2.8
* Differences between this value and the results obtained with the ISA system ar
the limit. Using the ISA system enables the distances travelledabove this limit to be considerably reduced without penalizingthe average speed because the system induces acceleration anddeceleration at more suitable points.
.2. Speeds on road sections with singular characteristics
To analyze traffic flow homogenization on singular roadtretches, speed was taken into account at four characteristic pointsn the singular stretches:
(A) Speed at deceleration starting point(B) Speed on entry to the limited safe speed zone(C) Minimum speedD) Speed on exiting curve
In addition, so that the speeds on the different curves could beompared, the speeds were compared to the safe speed limit. Theesults are presented in Table 5. As shown, the ISA system has aual effect: it reduces the mean values at every point, both on theurve and on its approach (but the differences are not statisticallyignificant when comparing the results with the results of nor-al driving), and it reduces the dispersion of the values between
rivers, which indicates that the traffic homogenizing effect isaintained. This homogenizing effect is also reflected in the num-
er of cases of high-speed variation on the singular stretch and inhe reduction of the differences in mean speed between drivers onhese stretches.
Finally, it was observed that there was a tendency to use therake pedal more in fast driving compared to normal driving. How-ver, we have seen no evidence that relaxed drivers use the brakeignificantly more to keep control of the vehicle when followinghe SAGE system’s instructions. Had this been the case, it wouldmply they were not accepting the speed pattern given by the sys-em because their actual speed was fast compared to their normalehavior.
.3. Path
It is well known that drivers do not follow the exact alignment ofhe curve (Glennon and Weaver, 1972). On the other hand, vehicleath and lateral placement on curves may be significant evidence ofnsafe traffic conditions (Steyer et al., 2000). In the tests conducted,he path was obtained by two means: GPS positioning (e.g., Ben-rieh et al., 2004; Castro et al., 2006; Imran et al., 2006) and inertialeasurement systems (e.g., Drakopoulos and Örnek, 2000). In order
o avoid signal degradation or loss in the first method, we decidedo take the results of the second method because this is sufficientlyccurate for the usual lengths of the sections analyzed (less than00 m) according to Jiménez et al. (2009).
As part of this study, path analysis is applied to examine whetherr not the system has any significant effect on the path, as well as toetect any anomalous behavior. Thus, the following aspects werehecked:
.0 −5.6 5.3 −1.1 5.7
stically significant (p < 0.05).
1. The minimum radius value falls within the boundary valuesgiven by the curve geometry. The curves are made up of tran-sition curves on entry to and exit from a circular curve with anapproximately constant radius. To set the boundary values forthe radius as an initial approximation, a simplification is usedthat only considers sections of constant radius and straight seg-ments. Thus, if we examine Fig. 8, it may be deduced that, to keepthe vehicle in the lane, the radius of the path followed by thecentre of the vehicle must be smaller than the following value:
Rmax = R + (Wl − Wv)
(12
+ cos(˛/2)1 − cos(˛/2)
)(5)
where R is the radius of the midline of the lane, Wl is the lanewidth, Wv is the vehicle width and is the change of directionproduced on the curve.
2. The vehicle travels the path without drifting into the other laneor the hard shoulder. To verify this, the detailed digital road mapused in the SAGE system was used.
3. The radius changes with no significant discrepancies comparedto the way the road radius changes. A qualitative analysis is
Fig. 8. Diagram of a constant radius curve for calculating the admissible boundaryvalues for the vehicle path radius.
46 F. Jiménez et al. / Accident Analysis and Prevention 48 (2012) 37– 48
Table 6Path check in the tests carried out without the SAGE system but with the second-generation system (10 drivers and 16 curves).
% cond. 1 is not fulfilled % cond. 2 is not fulfilled % cond. 3 is not fulfilled
* Differences between this value and the results obtained with the ISA system are stati
v2itecttsO(nfrstp
•
•
5
socs8
Fig. 9. Example of an anomalous evolution of a path radius.
Table 6 indicates the percentage of situations in which the pre-ious conditions are not fulfilled. It is evident that conditions 1 and
are not verified in a considerable number of cases of fast driv-ng (6.3% and 8.1%, respectively) because drivers tend to shortenhe path to maximize the radius, and thus they have lower lev-ls of lateral acceleration for the same speed or the ability to takeurves at higher speed without suffering higher lateral accelera-ions. The amount of non-compliance is therefore reduced withhe system active (1.3% and 3.1%, respectively) because the SAGEystem encourages more relaxed and uniform driving on curves.n the other hand, non-compliance with condition 3 is rare with
2.5%) or without (1.9%) the system. The main cause of the detectedon-compliances is entering a curve at excess speed for the path
ollowed, which forces the driver to rectify the path and, usually,educe speed on the curve itself (Fig. 9). There are two possiblecenarios given that successful travel along a curve depends uponhe appropriateness of the approach speed and adequate lateralositioning along the curve (Imran et al., 2006):
The path followed by the driver at the beginning of the curve iscorrect and the SAGE system has alerted them in advance thatthey should reduce speed, but the instructions have not been fol-lowed. This is the most common situation when dealing with aninformative ISA system.If the path followed by the driver is incorrect, the driver may drawa curve with a radius less than that predicted (Revue et al., 2003).This would involve a lower operating speed for which the SAGEsystem cannot provide advance warnings.
.4. Driver satisfaction with the redefined system
Finally, drivers were questioned on their satisfaction with theystem. The same questionnaire for assessing the first generation
f the system was now used; however, in this situation, they had toompare the old system and the redefined one. Even though theyhowed no more interest in including the system in their vehicles,
out of 10 drivers involved in the tests acknowledged an improve-
3.1 2.5
stically significant (p < 0.05).
ment in the system and were of the opinion that the warnings bettermet their expectations, particularly because of better adjustment ofthe safe speeds on the singular stretches and the way the warningswere given once braking had begun.
6. Discussion and conclusions
Some researchers maintain the theory that road transportsafety can be improved by the introduction of advanced tech-nologies. However, another group is of the opinion that educationand incentive-oriented policies are essential to influence driverbehavior because there is a compensatory change toward morerisk-taking behavior, which eliminates the increased safety fromtechnology (Marell and Westin, 1999) in line with homeostatic risktheory (Wilde, 1988). Houston et al. (1995) maintain that tech-nological progress leads to changes in risk-taking behavior, whichcan result from an individual’s altered perception of, and attitudestoward, risk. Risa (1994) has acknowledged the increase in safetydue to technological improvements but has argued that sometimesoffsetting behavior reduces the increase in safety as a result oftechnological improvements. Therefore, the potential benefits oftechnological progress are greater in the long term if they are freelyaccepted by drivers without any threat.
In the case of ISA systems, although most of the work has focusedon fixed limit systems, variable and dynamic limits could furnishmore positive effects. It is, however, necessary to consider that thewarnings should be coherent with an objective perception of therisk present in every circumstance depending on the conditions.That is, the system must behave like a driver who correctly receivesand processes a great amount of information at every instant.
In the study presented, a method to adapt the warnings ofan informative ISA system with variable speed limits has beendesigned, considering the low acceptance of the first generationsystem among drivers. It takes driving parameters into accountand has been applied to a small sample of drivers. Specific conclu-sions have been reached that have led us to make recommendationsfor redesigning the warning system concerning the safe speed oncurves and the deceleration process on the approach to a singu-lar section on single carriageway roads. These system changes arebased on experimental test results carried out with drivers in realdriving situations. One of the modifications is related to the speedon curves. In the first generation of the system, an upper limit forlateral acceleration had been fixed, but experimental data showedthat drivers accepted higher accelerations on curves of smallerradius. Furthermore, the length of the zone of influence of thecurve has been reduced, and the parameters of the decelerationprocess (deceleration levels and reaction time) have been changedto remove certain warnings that caused confusion to the driver.
In terms of acceptance and safety-related variables, such asspeed distribution, the results of the second generation of the SAGEsystem are positive and are an improvement to those obtained by
the first generation of the system (Jiménez et al., 2008). However,validation with larger samples is required, as well as an analysisof the long-term effects. Other future works involve measuring theworkload that the system produces on drivers and assessing other
ysis an
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nterfaces because the redefinition presented in this paper does notnclude changes to the HMI, but rather only changes to the warningarameters.
Finally, because there are differences in driving styles (e.g.,achmann, 1995), generalizing an ISA system for all drivers (withespect to safe speed limits or braking starting points, for instance)ould have a negative influence on their acceptance. Thus, it shoulde considered that, although some systems have been plannedith driver-selectable settings (Mayser et al., 2003), an open line of
esearch is to automatically identify the driving style. This knowl-dge could be used not only to adjust the ISA system parametersut also other ADAS parameters. However, this system customiza-ion would need to be analyzed to determine if it would result in
decrease in the speed homogenization achieved by the presentystem.
cknowledgment
The authors would like to express their gratitude to the groupsf voluntary drivers who took part in the tests.
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