Interaction Weaknesses of Personal Navigation Devices

Markus Hipp*, Florian Schaub*, Frank Kargl†, Michael Weber**Institute of Media Informatics

Ulm University89069 Ulm, Germany

{firstname.lastname}@uni-ulm.de

†DIES Research GroupUniversity of Twente

P.O. Box 2177500 AE Enschede, NLf.kargl@utwente.nl

ABSTRACTAutomotive navigation systems, especially portable navi-gation devices (PNDs), are gaining popularity worldwide.Drivers increasingly rely on these devices to guide them totheir destination. Some follow them almost blindly, withdevastating consequences if the routing goes wrong. Wrongmessages as well as superfluous and unnecessary messagescan potentially reduce the credibility of those devices. Weperformed a comparative study with current PNDs from dif-ferent vendors and market segments, in order to assess theextent of this problem and how it is related to the inter-action between device and driver. In this paper, we reportthe corresponding results and identify multiple interactionweaknesses that are prevalent throughout all tested deviceclasses.

Categories and Subject DescriptorsH.1.2 [User/Machine Systems]: Human factors; H.5.2[User Interfaces]: Evaluation/Methodology; H.5.2 [UserInterfaces]: Ergonomics

General TermsHuman Factors, Experimentation

KeywordsHCI, personal navigation devices (PND), study

1. INTRODUCTIONAutomotive navigation systems have become an assistanttechnology many drivers rely on. Navigation systems are ei-ther directly integrated in the vehicle or come as add-on solu-tions, like dedicated portable navigation devices (PNDs), orsmartphone software solutions. By navigation system we re-fer to a device or component that performs routing decisionslocally, in contrast to online routing services where some orall routing calculations are performed by servers online. Inthis work, we focus on PNDs, due to their widespread adop-tion and global market share [5].

Copyright held by authors.AutomotiveUI’10, November 11-12, 2010, Pittsburgh, Pennsylvania.ACM 978-1-4503-0437-5

Frequent drivers, business travelers, vocational drivers, andfamilies entrust their navigation systems with finding a suit-able route to their destinations. Nowadays, some drivers fol-low them almost blindly, i.e., by reacting instantaneously todriving directions announced by the device. Such blind trustcan lead to dangerous situations as underlined by anecdotalreports of people driving their cars into a river because theyfollowed the commands of their navigation system too liter-ally [7]. However, the case of a German driver who caused anaccident by turning around on a highway because his satel-lite navigation systems told him so [6] underlines the serious-ness of the problem. Hanowski et al. [4] were able to showthat the confidence placed in the commands of a navigationsystem and its credibility increases with the driver’s unfa-miliarity with the environment. They also showed that thedriver’s self-confidence decreases in such situations. Thus,the further away drivers are from their known environment,the more they rely on their navigation systems and trusttheir commands. In such situations drivers may also be-come susceptible to gullibility errors [3], which could resultin them acting upon erroneous commands that they wouldmost likely reject under normal conditions.

1.1 The Credibility IssueApparently, credibility is an important aspect of the problemat hand. Fogg and Tseng [3] distinguish between differenttypes of credibility of software systems. Presumed credibilityis based on general assumptions about the functionality ofa system, surface credibility is attributed based on the im-pression made by the hardware and interface design, reputedcredibility stems from the recommendations and opinions ofthird parties, while experienced credibility is based on earlierpersonal experiences. The first three aspects are prevalentin forming the initial credibility a driver attributes to thenavigation system. However, experienced credibility evolvesover time. While the first three credibility aspects in com-bination with unfamiliar environments may facilitate gulli-bility errors, the consequence of such an error will almostcertainly destroy experienced credibility of the navigationsystem. Mistrust towards future commands of the devicewill be the result.

In familiar environments, a slightly different situation un-folds. Drivers feel more confident and due to their back-ground knowledge can better judge the correctness of nav-igation commands, at least to a certain extent. There-fore, the danger of drivers acting upon erroneous commandsshould decrease. But in a familiar environment drivers also

form their own (not necessarily correct) belief what the bestroute would be in a given situation. A mismatch betweenthe planned route of the navigation system and the driver’sbelief of the best route is possible. Drivers may deliberatelydeviate from the suggested route to evade traffic jams orto make a purposeful detour, e.g., to stop at the grocerystore, or because they think they know a shortcut. Typi-cally, navigation systems react by issuing commands to leadthe driver back onto the planned route or a newly calcu-lated alternative route. But because the driver deliberatelydeviated from the route such messages will be consideredannoying especially if they occur repeatedly. It is likely thatsuch annoying or superfluous commands also have a negativeeffect on the experienced credibility of the device.

Another feature that may cause similar effects is the inte-gration of dynamic traffic information in the routing pro-cess. Some navigation systems can receive dynamic trafficmessages from different sources to inform the driver abouttraffic obstructions ahead and to suggest a detour. Whilepotentially a useful service, the presentation of such infor-mation can impact the experienced credibility of the system.For example, displaying the age of a traffic message wouldbe a useful indicator to judge its relevance and accuracy,while its absence may foster gullibility errors, if the naviga-tion system suggests a detour to avoid a traffic jam based oninformation that is hours old. Furthermore, the benefit ofa detour needs to be apparent to drivers and should matchtheir understanding of an appropriate alternative route.

Thus, the credibility of navigation systems can be reducedby gullibility errors, but superfluous or annoying messagescan also impact it negatively. The experienced credibilitycan potentially degrade to such a level that incredulity er-rors occur [3]. Drivers would ignore navigation commandsbecause they doubt their accuracy, even if following thesecommands would actually benefit them. This of course canlead to a further decrease of experienced credibility. In ad-dition, the exchange of negative experiences between cus-tomers can also decrease the reputed credibility of a prod-uct. Therefore it is in the best interest of vendors to ensurethat not only presumed and surface credibility remain highbut also experienced and reputed credibility.

1.2 Contribution & OutlineIn this work, we analyze the extent of interaction issues ofPNDs, which may cause credibility deterioration. In partic-ular, we focus on the frequency of superfluous and erroneousmessages and the integration of dynamic traffic warnings.We performed a comparative study with PNDs of differentvendors under real world conditions to assess their inter-action behavior with the driver. Based on the results, weidentified weaknesses in the interaction process and particu-larly problematic scenarios. As part of a structured researchapproach, these results will be used in future work to assesscredibility deterioration in navigation systems and to de-velop optimized interaction processes for such devices.

Section 2 provides background information on navigationsystems with a focus on information sources that impactrouting decisions and navigation commands. Section 3 out-lines the setup of our study, and Section 4 introduces theapplied metrics. Results are presented in Section 5. A dis-

cussion of these results and the identified weaknesses followsin Section 6. Section 7 concludes the paper.

2. BACKGROUNDNavigation systems draw information form multiple sourcesto make routing decisions. Besides the current position,which is usually determined with GPS, static map datastored in the device constitutes a main information source.Streets are represented by directed graphs, where nodes rep-resent intersections or fixed points on the map and edgesrepresent streets connecting nodes. Directed multigraphsare used to represent multiple lanes, i.e., two nodes can beconnected with multiple edges. Direction of a lane is repre-sented by edge direction. Additional attributes can expressthe type of streets, speed limits, etc. Edges are weighted. Inthe easiest implementation, a weight represents the lengthof a street segment, but weights may also be time-dependentto indicate different traffic situations over the day. The lat-ter enables devices to optimize routes for the current time ofday. To identify the optimal route, the cheapest path fromstart to finish has to be found. Different graph search algo-rithms exist to calculate these paths, for example, Djikstra’sSPF [2] or A* [1].

Another information source for PNDs are dynamic trafficmessages. Service providers as well as some PND vendors,like TomTom1 or Garmin2, collect driving information fromtheir users to generate time-dependent traffic information.TomTom devices with iQ-Routes come preloaded with adatabase filled with the information from over 10 billiondriven kilometers. This additional information is used tocalculate the most effective route for the driver dependingon the current time of day, which might not necessarily bethe shortest. Garmin offers nuLink! online services, whichprovides traffic information and Internet search functional-ity to models equipped with an integrated GSM module.

In addition to proprietary solutions, different services ex-ist to broadcast information about traffic jams, accidents,or other incidents to vehicles. The most common serviceis the Traffic Message Channel (TMC ), which is a partic-ular service of the Radio Data System (RDS), now undercontrol of the Traveller Information Services Association3

(TISA). TMC provides travel information via signals broad-cast over the FM-Data-Channel. TMCpro is an improvedderivative of the TMC service, operated as a commercialservice by Navteq.4 While TMC relies mainly on reports bydrivers, TMCpro utilizes real-time traffic data automaticallygenerated by data sensors, which are installed on highways.The process of extracting traffic warning from the gathereddata is automated and not subject to editorial control. Asthe extension possibilities of TMC and TMCpro are limited,the TPEG5 standard, also maintained by TISA, is able toprovide more precise information about traffic jams or acci-dents. TPEG will replace TMC as the standard service totransmit dynamic traffic information.

1TomTom website: http://www.tomtom.com/2Garmin website: http://www.garmin.com/3TISA website: http://www.tisa.org/4Navteq website: http://www.navteq.com/5Transport Protocol Experts Group

Navigation system manufacturers also provide other featuresto improve user interaction. For example, in order to pro-vide more precise announcements, some devices use text-to-speech technology to convert natural language text intospeech. This enables the inclusion of street names and otherdynamic attributes into announcements, which helps thedriver to relate the provided information to the real worldenvironment.

3. STUDY DESIGNThe goal of our study is the comparison of the interactionbehavior of different navigation systems under real worlddriving conditions. We focus on the frequency of naviga-tion commands and messages, as well as the quality of theprovided messages. See Section 4 for the employed metrics.But first, we describe the analyzed devices and the drivingscenarios of the study.

3.1 DevicesFor the study, only portable navigation devices (PNDs) havebeen considered as they constitute the most commonly usedtype of navigation systems [5]. Due to their portability theyalso facilitate comparison under the exact same conditions,as multiple PNDs can be installed in one car.

We want to gain a reasonable overview of the interactionconcepts currently prevalent in the market and, therefore,selected devices covering bottom-range ($), mid-range ($$),and top-range ($$$) segments of the German PND market.The analyzed devices are listed in Table 3.1. In the re-mainder of this paper, we will refer to the devices by theirdesignated labels.

Three of the five test devices support the reception of dy-namic traffic information via TMC or TMCpro. Tomtomfurther supports the iQ routes system, while Garmin onlysupports the Garmin operated nuLink! service. Pearl, asa bottom-range device, is the only navigation system thatdoes not come directly with a receiver for dynamic trafficinformation services. Note that text-to-speech is only sup-ported by the mid-range and top-range devices.

3.2 Driving ScenariosThe performed test drives were split into predefined drivingscenarios to analyze the behavior of the navigation systemsin different situations. During the test drives, the deviceswere mounted on a common board inside the vehicle. Asetup of two cameras monitored the audiovisual output ofthe navigation systems as well as the current road situation.Additionally, two persons transcribed navigation commandsand audiovisual messages issued by the devices. Fig. 1 de-picts the in-car setup. In preparation for each scenario, alltested devices were set to the same destination, and it wasverified that the devices calculated a similar route (Rc). De-pending on the driving scenario (see below) deliberate devi-ations from the route were planned and performed, resultingin a different driven route (Rd). The driven route Rd is sup-posed to reflect the intentions of a driver and the driver’sbelief of the correct route.

The driving scenarios have been designed to specifically an-alyze how the devices react in common driving situations.

Figure 1: In-car setup of the navigation systems.

Thereby, a balanced mixture of urban, suburban, rural, andhighway road usage was factored in.

Detour (city). The calculated route Rc is left for a de-liberate urban detour. A typical reason for such drivingbehavior would be the need to run quick errands before em-barking onto the actual journey, e.g. to stop by a grocerystore or drop children off at school. In this scenario, thedriven Rd and the calculated route Rc do not match. Themain purpose is to analyze how devices react to deviationsfrom the route. Road usage in this scenario is mainly urbanand suburban.

Highway. On the highway, commands of the navigationsystems are followed to analyze normal operation. In ad-dition, a stop at a petrol station is simulated to see hownavigation systems react.

Inner city. In this scenario, the routing behavior of thedevices close to a destination is analyzed. Devices shouldsupport the driver in finding the destination, yet providefreedom to search for a parking spot in proximity of thedestination. Urban roads dominate this scenario.

Detour (rural). Similar to the detour (city) scenario, thedriver deviates from the route on purpose. However, thedetour is chosen over rural roads in such a way that there isno obvious alternative route the PNDs could switch over to.A typical scenario for this case would be the spontaneousdecision to visit some touristic attraction or friends who liveclose to the original route.

Dynamic traffic warning. Here, we deliberately travel achosen route during rush hour with many traffic jams alongthe route. The aim of this scenario is the analysis of theintegration of dynamic traffic warnings. Of special inter-est is the presentation of the warning message, associatedinformation, and potential alternatives to the driver.

The first four scenarios were combined in one route fromUlm University, just outside the town of Ulm, to a loca-tion in the inner city of Munich, and back. Fig. 2 gives an

Table 1: Analyzed DevicesLabel Manufacturer and model Text-to-speech Dynamic traffic services Price segmentBecker Becker Traffic Assist Z205 yes TMC, TMCpro $$$Garmin Garmin nuvi 1690 yes nuLink! $$$Falk Falk F6 yes TMC, TMCpro $$Tomtom TomTom XL Europe yes TMC, iQ Routes $$Pearl Pearl V35-1 no - $

detour(city)

detour(rural)

highway

innercity

Figure 2: The calculated route Rc (blue) and the different scenarios (red). Map data: OSM.org

overview of the planned route Rc (blue), the different sce-narios, and the driven route Rd (red). The dynamic trafficwarning scenario was performed and tested separately, asone had to dynamically look for suitable traffic jams.

4. METRICSTo allow an objective comparison of the different devicesunder test, we define specific metrics for measuring theirbehavior. First of all, we distinguish between acoustic andvisual messages and navigation commands. Visual messagescontain all information that is shown on the display. Acous-tic messages are voice instructions from the system, whicheither support visual messages or provide distinct informa-tion. A navigation command may consist of an acoustic orvisual message, or both. For example, a typical navigationcommand for turning right in 300 meters would consist ofthe acoustic message “turn right in 300 meters” and a visualmessage on the display composed of an arrow pointing tothe right and the distance below or next to it. Animationsthat convey device activity are also counted as visual mes-sages, e.g. two animated turning arrows when the route isrecalculated.

In the analysis, we distinguish observed navigation com-mands according to message correctness and driver antic-ipation. We define message correctness to reflect the cor-rectness of the issued navigation command in terms of thedriver’s intention. This means a message is correct, if itscontent conforms with and supports the driver’s belief ofthe correct route. All other messages are labeled false. Inour study, all events that correspond to the route intendedby the driver Rd are labeled as correct.

Figure 3: Definition of four message categories.

Driver anticipation measures if the driver can expect themessage or if the message comes as a surprise, i.e., is unex-pected. Expected messages are predictable in the sense thatthey fit the current situation and align with the behaviorof the driver, but not necessarily with the driver’s intention.They constitute the majority of all messages. All other mes-sages appear unexpected. In our study, a message is labeledexpected if it supports the calculated route Rc in the currentsituation.

Fig. 3 depicts the two axes and the four possible message cat-egories. Correct/expected messages constitute desired nav-igation commands. Correct/unexpected messages providecorrect information but are not anticipated in the currentsituation, e.g., the message “follow the road for 60 kilome-

false correct

expected

unexpected

41% 56%

2%1%

Figure 4: Average distribution of all observed mes-sages.

ters” is correct but unexpected if the driver has been follow-ing this road for several kilometers already. False/expectedmessages occur when the driver deviates from the route. Themessage content does not conform to the driver’s intention,but due to explicitly leaving the route such messages canbe anticipated by the driver, e.g., repeating “turn around”messages. The instruction to turn around while driving on ahighway falls in the category of false/unexpected messages.

5. RESULTSIn the following, we present the results of the performedstudy and analyze the behavior of the tested devices foreach driving scenario.

In total, 162 kilometers where driven in 2:06 hours. In thistime, 189 acoustic messages and 191 visual messages havebeen observed. Due to GPS radio reception problems, theTomtom device had to be replaced to the side window, whereit could not be captured by the camera anymore. However,the behavior of the Tomtom device was still observed andrecorded manually.

The cumulative observation results over all devices and thefirst four driving scenarios are given in Fig. 4. Unexpectedmessages account only for 3% of all messages. The rea-sons for their occurrence are GPS radio reception problems.Thus, these messages are indeed dangerous but relativelyrare. False/expected messages account for 41% of all ob-served messages, while merely 56% of all messages were cor-rect/expected. While the extreme nature of the chosen driv-ing scenarios degraded these values compared to a normaldrive, it shows that PNDs react inflexible to changing driverintentions. Interestingly, all devices behaved quite similarirrespective of their price segment.

A detailed analysis of each driving scenario is provided inthe following, highlighting typical reactions and behavior.The cumulative results for each scenario are summarized inFig. 5.

5.1 Detour (city)The 16 km long scenario was completed in 24 minutes. Asexpected, all devices called on the driver to turn around,as soon as the driver deviated from the planned route. TheFalk called on the driver to turn around seven times (Becker

five times, Pearl three times). As acoustic messages, Pearland Becker used phrases like“turn left, then turn left”. OnlyFalk used the words “please turn around”.

With 41 acoustic messages, the Pearl was most active inthis scenario, while the Becker acted very passive, with onlysix acoustic messages. Falk and Pearl produced nine falsemessages each, Becker only produced seven. Messages werefalse in the sense that they did not correspond to the driver’sintention. Falk and Pearl recalculated their route nine times(indicated by visual messages). In case of the Pearl two ofthe recalculations resulted from GPS radio reception prob-lems. The Becker performed six recalculations.

Fig. 5(a) gives the cumulative results for this scenario. Thehigh percentage of correct/expected messages (57%) resultsfrom the fact that all devices stopped issuing turn aroundmessages and reverted to an alternative route R′

c as soon asthat route became shorter than he orignal route Rc. Subse-quently, mainly correct/expected messages were issued, be-cause R′

c corresponded to Rd. Thus, the 40% false/expectedmessages occurred almost completely in the first minutes ofthis scenario.

5.2 HighwayIn this scenario, we analysed the devices’ behaviour during ahighway trip, where the driver followed the calculated route,i.e., the driven route Rd matched the calculated route Rc.As expected, all navigation systems remained passive andcalm during the whole trip of 123 kilometers (76 minutes).The average number of visual messages was 10.6, from which74.4% have been correct (see Fig. 5(b)). There have beenan average of 4.3 acoustic messages during the scenario. Atone point, shortly before the end of the scenario, the calcu-lated route Rc of two PNDs differed from the others and theintended route Rd.

To simulate the refueling at a petrol station, the highwaywas left once. Due to the fact that the gas station was lo-cated near the shoulder of the highway, none of the devicescorrected the displayed position but continued showing thecar on the highway. When the trip was resumed, one de-vice temporarily displayed an erroneous position and orien-tation for the car. Because the device believed the vehicleto be driving in the opposite direction, the driver was in-structed to leave the highway at the next exit to turn around(false/unexpected message).

5.3 Inner cityDuring the inner city scenario (17 km, 19 minutes), reli-able routing by all devices was observed. 92.5% of visualmessages and all of the acoustic messages were correct (seeFig. 5(c)). The precise acoustic messages provided by theFalk device near intersections are notable. Announcementsincluded the indication of direction and street names. Forexample, “In 500 meters, leave the highway towards B13Munich, therefore keep right” in contrast to instructions like“After 800 meters, keep right, then keep left” issued by otherdevices. After reaching the destination, which all devicesannounced acoustically, all devices suspended routing andswitched into free drive mode to support the driver in thesearch for parking.

false correct

expected

unexpected

40% 57%

1%2%

(a) Detour (city)

false correct

expected

unexpected

13% 83%

1%1%

(b) Highway

false correct

expected

unexpected

4%

95%

0% 1%

(c) Inner city

false correct

expected

unexpected0% 0%

93% 7%

(d) Detour (rural)

Figure 5: Message Distribution for the different driving scenarios.

5.4 Detour (rural)In this scenario, the driver deviated from the calculatedroute Rc onto rural roads. Roads with few intersectionswere chosen to reduce potential alternative routes the de-vices could change to. The detour extended over 6 km (7minutes) before the driver turned around eventually. In thistime, 6.3 route recalculations were performed on average.This corresponds to 1.16 recalculations per kilometer byBecker and Pearl, and 0.83 recalculations by Falk. Noneof the devices explicitly informed the driver about the rea-son of recalculation. Instead, turn around commands wererepeatedly issued.

In combination with the large number of messages (oneacoustic message every 37 seconds, one visual message ev-ery 57 seconds, on average) the behavior suggests, that thedriver is rather stressed than supported by the devices.

5.5 Dynamic traffic warningThe integration of dynamic traffic warnings by the PNDshas been analyzed independently of the other four scenar-ios. We analyzed the behavior of devices in situations wherewarnings about traffic obstructions on the planned route Rc

appeared. Hereby, the focus was placed on the informationthe devices provide about the obstruction and how it is con-veyed, as well as the decision support provided to the driver.

Due to different information sources for the dynamic traf-

fic warnings, the listing of traffic information varied betweendevices, e.g., between Becker (TMC) and Garmin (nuLink!).Additionally, TMC messages can also differ strongly betweendevices, because, in Germany, radio broadcasting corpora-tions are responsible for creating and updating traffic infor-mation. Each broadcasting corporation relies on it’s ownmix of different information sources, e.g., listeners, automo-bile associations like the German ADAC6, or police notifi-cations.

If traffic warnings existed, the driver could call up a list ofreceived warnings and their information on all devices. Adetailed view for each message provides the reason for theobstruction (traffic jam, accident, etc.) and displays the ap-proximate position of the obstruction. None of the testeddevices provided information about a message’s time of ori-gin. When a dynamic traffic warning for an obstruction onthe planned route Rc is received, the tested devices call onthe the driver to decide if the traffic jam should be circum-vented.

As an example, Fig. 6 show the dialog of the Becker device.A traffic jam is depicted as a purple area on the plannedroute (red). A symbol on the right depicts the type of ob-struction. The navigation system provides only one alterna-tive to avoid the obstruction. Only approximations of the

6ADAC – Allgemeiner Deutscher Automobil-Club e. V.:http://www.adac.de

Figure 6: Recommended route to circumvent thetraffic jam.

Figure 7: Display of an obstruction of traffic and thepossibility to avoid this route.

saved time and the distance difference of the detour are pro-vided as additional information. How the value of, in thiscase, 28 minutes has been determined is not obvious to thedriver and not explained by the system. If additional in-formation is available for the calculation of the saved time,e.g., characteristics of the traffic jam, it is not displayed tothe driver. In the example, it is also not obvious and notexplained, why this exact route (yellow) is recommendedto avoid the traffic jam, while several other (presumablyshorter) routes are discernible on the map (see Fig. 6).

However, other devices, e.g., the Garmin, do not even dis-play the alternative route to the driver. Fig. 7 shows thatthe only information available to support the driver’s deci-sion is the length of the traffic jam (0.1 km) and the causeof the obstruction (construction site). Thereby, the degreeof obstructions is displayed in different colors, e.g., greenindicates a slight obstruction and red indicates a heavy ob-struction.

In general, the tested devices provide not enough informa-tion about the obstruction and possible alternatives to en-able the driver to make an informed decision about stayingon the route or avoiding the obstruction. Unfortunately, thetested devices did also not provide a recommendation forthe best choice.

6. DISCUSSIONIn this Section, the results from the study are discussed andinteraction weaknesses are summarized.

Currently, a driver can only influence the route prior to atrip by choosing between different route characteristics, e.g.,shortest, fastest, or most economic route. Furthermore, thedriver can choose to avoid toll roads or highways. Basedon these adjustments, the navigation system calculates theroute. At this point no reasons are given why this particularroute is chosen, and the driver has no opportunity to com-pare this route to other alternatives. A comparison maybeperformed internally by the system, but it is invisible tothe driver. Almost all devices instantly started the routeguiding. Only the Pearl displayed a general overview ofthe planned route before commencing navigation. While in-stantly switching to route guiding might save time, display-ing a route overview can prevent mistakes due to wronglyselected destinations.

The study showed that PNDs are reliable and perform wellin normal situations, when the calculated route Rc matchesthe intended route Rd. However, while all PNDs issued cor-rect and expected messages for navigation commands, sev-eral devices produced cryptic messages. Here, the top rangedevices performed notably better.

When the driver deviates from the calculated route Rc onpurpose, the devices are not able to dynamically adapt to thedriver’s decision. The driver is not properly informed aboutthe deviation detected by the device, route recalculationsoccur mostly silently and are only accompanied by shortvisual messages (e.g. turning progress arrows). The navi-gation systems instruct the driver to turn around but giveno indication why, i.e., to return to the calculated route Rc.Interestingly, only one device actually used the vocal instruc-tion “turn around”, while the others resorted to sequences ofturn commands (“turn left, then left”). The latter behaviornot only increases the number of messages but also does notconvey the intention of the device to the driver. Further-more, the driver has no possibility to inform the navigationsystem about its intention (deviation from the route) with-out performing multiple clicks in touch menus to abort therouting process.

The study showed further that in both scenarios where thedriver left the route intentionally the navigation systemsproduced 56% false acoustic and 65% false visual messagesin total, respectively. While those messages can be expectedby the driver, because they intentionally left the route, theobserved frequency and persistence of such messages sug-gests increased stress for the driver in already stressful sit-uations. In contrast, the numbers of correct messages wereconsiderable higher in scenarios, where the driver followedthe calculated route (93% correct acoustic and 88% correctvisual messages).

Another critical point is the interaction concerning dynamictraffic warnings. While tested devices that support the re-ception of dynamic traffic warnings notified the driver aboutobstructions on the planned route, the provided informationis often not sufficient for the driver to make an informed deci-sion if the obstruction should be avoided or not. The driver’soptions for interaction are also very limited. The driver caneither accept the calculated detour (which is not displayedby all devices) or continue on the current route. It remainsto be analyzed what information needs to be displayed to

actually support the driver in this decision and to improvesystem credibility in such scenarios.

In summary, major interaction weaknesses could be identi-fied for the route selection, the purposeful deviation fromroutes, and the integration of dynamic traffic warnings. Al-though the study included devices of different price ranges,the issues were prevalent throughout all devices with minorvariations.

We conclude that in order to improve credibility, the inter-action between the driver and the system must be improved.The driver should be able to comprehend system decisions,e.g., why a specific alternative route has been selected andnot another. At the same time, devices should enable thedriver to interact with them in order to convey changes indriving intention. With improved bidirectional informationinterchange, the amount of false messages could be poten-tially reduced and the devices could better adapt to drivers.

7. CONCLUSIONIn this work, we studied the interaction behavior of severalcurrent personal navigation devices. We designed drivingscenarios to analyze the behavior of PNDs from differentcategories in specific situations. All devices have been testedin parallel to ensure the same conditions.

The results of the study support our hypothesis that PNDsperform well as long as drivers follow their directions, but failin deviating situations. As soon as the driver leaves the cal-culated route, all devices show interaction weaknesses. Theyissue cryptic commands (“turn left, then left”) repeatedly,without communicating the actual problem to the driver,e.g., that the driver left the calculated route. While this be-havior is desired to some extent when the driver leaves theroute by accident, it becomes an annoyance in situationswhere the driver acts on purpose. Current PNDs are notflexible enough to cater for both situations. We argue thatthe root cause of these problems is the lack of interactivitywith the driver en route. While vendors constantly work toimprove routing functionality, as well as output mechanisms(e.g., text to speech), what is missing in today’s devicesare information exchange loops between driver and device.The navigation system does not expose enough informationabout its internal decisions. Instead, they should offer mean-ingful information about commands and route alternativesto empower drivers to understand why a certain route hasbeen recommended. Otherwise navigation systems risk theloss of credibility with the driver.

At the same time, drivers have neither explicit nor implicitmeans to communicate their dynamic intentions to the nav-igation device.

They can only influence routing beforehand by specifying avague route preference, e.g. fastest route. We claim thatsuch static settings are not sufficient to capture a person’sdriving habits. Instead, navigation systems should utilizeavailable sensors to implicitly infer the driving behavior overtime and adapt accordingly. Furthermore, by offering mean-ingful interaction opportunities and alternatives, navigationsystems could engage in a dialog with the driver, betteradapt to the driver, and maintain high credibility. However,great care is required in interaction design to ensure thatthe driver can concentrate on the primary task – operatingthe vehicle.

We are currently developing and evaluating an interactionmodel for navigation systems. Our model facilitates dy-namic adaptation to driver intentions and addresses the is-sues uncovered in this work by improving the credibility ofinteraction processes.

8. ACKNOWLEDGMENTSThe authors would like to thank Bjorn Wiedersheim for hissupport during the test drive, the manufacturers and retail-ers that kindly provided the tested devices, and the anony-mous reviewers for their valuable comments. This work wassupported by Transregional Collaborative Research CentreSFB/TRR 62 (“Companion-Technology for Cognitive Tech-nical Systems”) funded by the German Research Foundation(DFG).

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