Interaction Weaknesses of Personal Navigation Devices Markus Hipp*, Florian Schaub*, Frank Kargl���, Michael Weber* *Institute of Media Informatics Ulm University 89069 Ulm, Germany {firstname.lastname}@uni- ulm.de ���DIES Research Group University of Twente P.O. Box 217 7500 AE Enschede, NL f.kargl@utwente.nl ABSTRACT Automotive navigation systems, especially portable navi- gation devices (PNDs), are gaining popularity worldwide. Drivers increasingly rely on these devices to guide them to their destination. Some follow them almost blindly, with devastating consequences if the routing goes wrong. Wrong messages as well as superfluous and unnecessary messages can potentially reduce the credibility of those devices. We performed a comparative study with current PNDs from dif- ferent vendors and market segments, in order to assess the extent of this problem and how it is related to the inter- action between device and driver. In this paper, we report the corresponding results and identify multiple interaction weaknesses that are prevalent throughout all tested device classes. Categories and Subject Descriptors H.1.2 [User/Machine Systems]: Human factors H.5.2 [User Interfaces]: Evaluation/Methodology H.5.2 [User Interfaces]: Ergonomics General Terms Human Factors, Experimentation Keywords HCI, personal navigation devices (PND), study 1. INTRODUCTION Automotive navigation systems have become an assistant technology 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), or smartphone software solutions. By navigation system we re- fer to a device or component that performs routing decisions locally, in contrast to online routing services where some or all routing calculations are performed by servers online. In this work, we focus on PNDs, due to their widespread adop- tion and global market share [5]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. AutomotiveUI���10, November 11-12, 2010, Pittsburgh, Pennsylvania. ACM 978-1-4503-0437-5 Frequent drivers, business travelers, vocational drivers, and families 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 to driving directions announced by the device. Such blind trust can lead to dangerous situations as underlined by anecdotal reports of people driving their cars into a river because they followed the commands of their navigation system too liter- ally [7]. However, the case of a German driver who caused an accident 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 show that the confidence placed in the commands of a navigation system and its credibility increases with the driver���s unfa- miliarity with the environment. They also showed that the driver���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 trust their commands. In such situations drivers may also be- come susceptible to gullibility errors [3], which could result in them acting upon erroneous commands that they would most likely reject under normal conditions. 1.1 The Credibility Issue Apparently, credibility is an important aspect of the problem at hand. Fogg and Tseng [3] distinguish between different types of credibility of software systems. Presumed credibility is based on general assumptions about the functionality of a system, surface credibility is attributed based on the im- pression made by the hardware and interface design, reputed credibility stems from the recommendations and opinions of third parties, while experienced credibility is based on earlier personal experiences. The first three aspects are prevalent in forming the initial credibility a driver attributes to the navigation system. However, experienced credibility evolves over 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 almost certainly destroy experienced credibility of the navigation system. Mistrust towards future commands of the device will 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 commands should decrease. But in a familiar environment drivers also

form their own (not necessarily correct) belief what the best route would be in a given situation. A mismatch between the planned route of the navigation system and the driver���s belief of the best route is possible. Drivers may deliberately deviate from the suggested route to evade traffic jams or to make a purposeful detour, e.g., to stop at the grocery store, or because they think they know a shortcut. Typi- cally, navigation systems react by issuing commands to lead the driver back onto the planned route or a newly calcu- lated alternative route. But because the driver deliberately deviated from the route such messages will be considered annoying especially if they occur repeatedly. It is likely that such annoying or superfluous commands also have a negative effect 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 traffic messages from different sources to inform the driver about traffic obstructions ahead and to suggest a detour. While potentially 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 would be 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 on information that is hours old. Furthermore, the benefit of a detour needs to be apparent to drivers and should match their understanding of an appropriate alternative route. Thus, the credibility of navigation systems can be reduced by gullibility errors, but superfluous or annoying messages can also impact it negatively. The experienced credibility can potentially degrade to such a level that incredulity er- rors occur [3]. Drivers would ignore navigation commands because they doubt their accuracy, even if following these commands would actually benefit them. This of course can lead 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 ensure that not only presumed and surface credibility remain high but also experienced and reputed credibility. 1.2 Contribution & Outline In this work, we analyze the extent of interaction issues of PNDs, which may cause credibility deterioration. In partic- ular, we focus on the frequency of superfluous and erroneous messages and the integration of dynamic traffic warnings. We performed a comparative study with PNDs of different vendors under real world conditions to assess their inter- action behavior with the driver. Based on the results, we identified weaknesses in the interaction process and particu- larly problematic scenarios. As part of a structured research approach, these results will be used in future work to assess credibility deterioration in navigation systems and to de- velop optimized interaction processes for such devices. Section 2 provides background information on navigation systems with a focus on information sources that impact routing decisions and navigation commands. Section 3 out- lines the setup of our study, and Section 4 introduces the applied metrics. Results are presented in Section 5. A dis- cussion of these results and the identified weaknesses follows in Section 6. Section 7 concludes the paper. 2. BACKGROUND Navigation systems draw information form multiple sources to make routing decisions. Besides the current position, which is usually determined with GPS, static map data stored 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 edges represent streets connecting nodes. Directed multigraphs are used to represent multiple lanes, i.e., two nodes can be connected with multiple edges. Direction of a lane is repre- sented by edge direction. Additional attributes can express the type of streets, speed limits, etc. Edges are weighted. In the easiest implementation, a weight represents the length of a street segment, but weights may also be time-dependent to indicate different traffic situations over the day. The lat- ter enables devices to optimize routes for the current time of day. To identify the optimal route, the cheapest path from start to finish has to be found. Different graph search algo- rithms exist to calculate these paths, for example, Djikstra���s SPF [2] or A* [1]. Another information source for PNDs are dynamic traffic messages. Service providers as well as some PND vendors, like TomTom1 or Garmin2, collect driving information from their users to generate time-dependent traffic information. TomTom devices with iQ-Routes come preloaded with a database filled with the information from over 10 billion driven kilometers. This additional information is used to calculate the most effective route for the driver depending on the current time of day, which might not necessarily be the shortest. Garmin offers nuLink! �� online services, which provides 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 service is the Traffic Message Channel (TMC), which is a partic- ular service of the Radio Data System (RDS), now under control of the Traveller Information Services Association3 (TISA). TMC provides travel information via signals broad- cast over the FM-Data-Channel. TMCpro is an improved derivative of the TMC service, operated as a commercial service by Navteq.4 While TMC relies mainly on reports by drivers, TMCpro utilizes real-time traffic data automatically generated by data sensors, which are installed on highways. The process of extracting traffic warning from the gathered data is automated and not subject to editorial control. As the extension possibilities of TMC and TMCpro are limited, the TPEG5 standard, also maintained by TISA, is able to provide more precise information about traffic jams or acci- dents. TPEG will replace TMC as the standard service to transmit 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