In-vehicle Information Systems Research Articles

The effects of age, interface modality, and system design on drivers’ attentional demand when making phone calls while driving on a limited-access highway

In-vehicle information systems that allow drivers to use a single voice command to complete a task rather than multiple commands better keep drivers’ attention toward the road, especially compared with when drivers complete the task manually. However, single voice commands are longer and more complex and may be difficult for older drivers to use. The current study examined the glance behavior, workload, and driving performance of drivers age 20–66 years when they placed a call using their hands or voice with the Chevrolet MyLink or Volvo Sensus information system during highway driving. In general, as age increased, drivers took longer to complete phone calls, reported greater workload when using voice commands, and made significantly more off-road glances lasting longer than two seconds when placing calls relative to younger drivers. Both the voice-command systems of MyLink and Sensus increased the proportion of time that drivers were looking at the road when calling compared with manual phone calling, but the relative increase was greater when using MyLink’s single-voice-command system compared with the multiple-command system of Sensus, and this advantage grew as drivers aged. The findings indicate that placing calls while driving using voice commands helps drivers of all ages keep their attention toward the road better than doing so manually, and that, contrary to expectation, using a single-command system like MyLink’s worked better than a multiple-command system like Sensus for older drivers as well as younger ones.

Impact of Screen Size and Structure of IVIS on Driving Distraction

With the development of in-vehicle information system (IVIS), more and more touchscreens have been applied to the IVIS to upgrade driving experience. During the process of using IVIS, driver needs to identify complex information system and process the information, thereby prolonging the reaction time of the driving behaviour, and the IVIS causes the driver to distract the attention and shift the sight. In order to reduce the impact of IVIS on driving distraction and ensure driving safety, this paper studies screen size and information structure of IVIS. In this paper, we designed nine kinds of IVIS for simulation in urban road and urban highway driving scenarios, and collected the data of relevant driving performance and eye movements. Based on experiment data, this paper evaluates and analyses the strategy of screen size and structure of IVIS for use safety. This paper is expected to provide reference for the design of the touch screen of the IVIS.

Perceived Safety Benefits of Aftermarket Driver Support Systems: Results from a Large Scale European Field Operational Test (FOT)

A field operational test (FOT) is a technique used within traffic safety to evaluate the overall value of in-vehicle information systems (IVISs) under normal operating conditions. In this study, a pan-European FOT was used to evaluate Navigation, Speed Information/Alert, Traffic Information, and Green Driving Support functions together with participants’ perceptions of safety’ before, during, and after using the functions. Through utilization and adherence to the FOT methodology, data were collected over a period ranging from 8 to 16 months in five European countries in order to assess the driver pre-conceived ideas and subsequent subjective and objective experiences with the IVIS functions. Several analyses of data were conducted, and this paper describes the results relating to the ‘user-experience’ as evaluated through subjective responses. The study showed that before the FOTs started, overall participants expected a higher safety benefit through using Speed Alert compared to the other functions. This function was also perceived to offer the highest safety benefit after the FOT had been completed. Perceptions of safety were found to be lowest for the green-driving function. The results offer insights into public expectations of IVIS functions and how these change with experience and overall; they suggest that, in some cases, the perception to safety benefits could be somewhat misplaced.

Enabling multitasking by designing for situation awareness within the vehicle environment

In the driving environment, competition exists between Driving Related Activities (DRAs) and Non-Driving Related Activities (NDRAs). This is a source of inattention and human error. Continual proliferation of in-vehicle information systems (IVIS) presents drivers with opportunities for distraction. Drivers simultaneously manage DRAs alongside unrelated but cognitively demanding NDRAs. Vehicle designers need ways of understanding human capability in such situations to provide solutions that accommodate these conflicting demands. This paper proposes a framework intended to address such challenges, rooted in the widely accepted construct of Situation Awareness (SA). However, SA theory does not presently accommodate disparate unrelated goal-driven tasks performed in parallel. This framework reconciles the present reality of drivers simultaneously devoting cognitive resources to attain SA for multiple activities by proposing a separate body of knowledge for each active goal. Additionally, the process of achieving SA is expanded to incorporate this concurrent development of separate bodies of goal-directed knowledge. The advantage of reconceptualising SA for driving allows consideration of interface design which minimises the impact of competing activities. The aim is a framework facilitating creation of IVIS that help drivers succeed in multi-goal multitasking situations. Implications of the proposed framework for theory, design and industry-driven automotive safety efforts are discussed.

Development and Evaluation of a Computational Human Performance Model of In-vehicle Manual and Speech Interactions

Usability evaluation traditionally relies on costly and time-consuming human-subject experiments, which typically involve developing physical prototypes, designing usability experiment, and recruiting human subjects. To minimize the limitations of human-subject experiments, computational human performance models can be used as an alternative. Human performance models generate digital simulations of human performance and examine the underlying psychological and physiological mechanisms to help understand and predict human performance. A variety of in-vehicle information systems (IVISs) using advanced automotive technologies have been developed to improve driver interactions with the in-vehicle systems. Numerous studies have used human subjects to evaluate in-vehicle human-system interactions; however, there are few modeling studies to estimate and simulate human performance, especially in in-vehicle manual and speech interactions. This paper presents a computational human performance modeling study for a usability test of IVISs using manual and speech interactions. Specifically, the model was aimed to generate digital simulations of human performance for a driver seat adjustment task to decrease the comfort level of a part of driver seat (i.e., the lower lumbar), using three different IVIS controls: direct-manual, indirect-manual, and voice controls. The direct-manual control is an input method to press buttons on the touchscreen display located on the center stack in the vehicle. The indirect-manual control is to press physical buttons mounted on the steering wheel to control a small display in the dashboard-cluster, which requires confirming visual feedback on the cluster display located on the dashboard. The voice control is to say a voice command, “ deflate lower lumbar” through an in-vehicle speaker. The model was developed to estimate task completion time and workload for the driver seat adjustment task, using the Queueing Network cognitive architecture (Liu, Feyen, & Tsimhoni, 2006). Processing times in the model were recorded every 50 msec and used as the estimates of task completion time. The estimated workload was measured by percentage utilization of servers used in the architecture. After the model was developed, the model was evaluated using an empirical data set of thirty-five human subjects from Chen, Tonshal, Rankin, & Feng (2016), in which the task completion times for the driver seat adjustment task using commercial in-vehicle systems (i.e., SYNC with MyFord Touch) were recorded. Driver workload was measured by NASA’s task load index (TLX). The average of the values from the NASA-TLX’s six categories was used to compare to the model’s estimated workload. The model produced results similar to actual human performance (i.e., task completion time, workload). The real-world engineering example presented in this study contributes to the literature of computational human performance modeling research.

Inaccuracy versus volatility – Which is the lesser evil in battery electric vehicles?

Due to the limited range of battery electric vehicles, research has emphasized a frequently observed dimension of driver stress referred to as a concern of becoming stranded due to a depleted battery known as range stress. It has been suggested that the appropriate provision of range-related information through in-vehicle information systems constitutes a promising solution to overcome range stress. However, drivers often struggle to understand the influencing factors of the displayed range in battery electric vehicles and thus lose trust in the range estimation. Building on field experiments in real traffic situations, we aim to investigate the influence of two range gauges typically present in battery electric vehicles, which differ in terms of information accuracy and displayed volatility, on the perception of trust in the estimates and range stress. In addition, we aim to explore how trust in the range estimates influences the perception of range stress and how trust and range stress affect the acceptance of battery electric vehicles. We found that displaying accurate but volatile range information results in higher perception of range stress and a lower feeling of trust in comparison to the provision of less accurate but less volatile range information. In addition, we observed that higher levels of trust in range estimates leads to lower range stress and higher acceptance of battery electric vehicles. Finally, we showed that range stress is negatively related to battery electric vehicle acceptance.

Distractions intervention strategies for in-vehicle secondary tasks: An on-road test assessment of driving task demand based on real-time traffic environment

When driving a vehicle, the driver must allocate adequate attention to the demands of driving in order to be safe. Based on analyses of vehicle data, driving environment data, and videos of the road ahead, a driving task demand prediction model based on real-time road traffic data was established in this study. Assessments of driving task demands allowed for the validation of this model. In addition, intervention strategies were proposed for distractions from in-vehicle secondary tasks at different levels of demand. The analysis showed that at a high driving task demand, the control of all in-vehicle information systems (IVIS), except for the playing of radio or CD, should be warned against or forbidden. Meanwhile, distractions from secondary tasks originated from 91% of the in-vehicle foreign objects, and 67% of the in-vehicle facilities can be avoided by formulating precaution strategies. This study provides methods and technical support for the management of driver distraction precaution.

A Predictive Fingerstroke-Level Model for Smartwatch Interaction

The keystroke-level model (KLM) is commonly used to predict the time it will take an expert user to accomplish a task without errors when using an interactive system. The KLM was initially intended to predict interactions in conventional set-ups, i.e., mouse and keyboard interactions. However, it has since been adapted to predict interactions with smartphones, in-vehicle information systems, and natural user interfaces. The simplicity of the KLM and its extensions, along with their resource- and time-saving capabilities, has driven their adoption. In recent years, the popularity of smartwatches has grown, introducing new design challenges due to the small touch screens and bimanual interactions involved, which make current extensions to the KLM unsuitable for modelling smartwatches. Therefore, it is necessary to study these interfaces and interactions. This paper reports on three studies performed to modify the original KLM and its extensions for smartwatch interaction. First, an observational study was conducted to characterise smartwatch interactions. Second, the unit times for the observed interactions were derived through another study, in which the times required to perform the relevant physical actions were measured. Finally, a third study was carried out to validate the model for interactions with the Apple Watch and Samsung Gear S3. The results show that the new model can accurately predict the performance of smartwatch users with a percentage error of 12.07%; a value that falls below the acceptable percentage dictated by the original KLM ~21%.

Event-related potentials as indices of mental workload while using an in-vehicle information system

New in-vehicle information systems are now being commercialized. Despite the expected benefits, some concerns exist that they may overload drivers’ capacity and decrease performance. According to the multiple resource theory (Wickens, Hum Factors 50:449–455, https://doi.org/10.1518/001872008X288394, 2008), overload may occur at different stages of processing, that is, perceptual–central and/or response-related stages. Therefore, different measures may be needed to detect such specific demands. We explored the sensitivity of different mental workload measurements during the performance of an auditory task alone (single task) and in combination with a tracking task that was presented without (dual task) or, with a visual display (triple task). The demands associated with the number of concurrent tasks (single, dual and triple tasks), tracking speed (low, high, adjustable) and their interaction were analyzed. To account for different processing requirements, mental workload was assessed using subjective, behavioral (performance on the auditory task) and psychophysiological measurements (event-related potentials). 17 young adults participated in the study. The results showed that most measurements discriminated between the performances of one or more tasks, as well as between low and high speeds. However, only the subjective ratings and tracking task performance further discriminated between the dual- and triple-task conditions. Finally, ERPs (N1 and P3) were the only measure detecting increases in cognitive demands associated with higher requirements on processing speed combined with the addition of the display. Our results suggest that ERPs may provide complementary information to other traditional mental workload measures. Its applications in the evaluation and design of future systems should be investigated.

Toward Intelligent Vehicle Intrusion Detection Using the Neural Knowledge DNA

ABSTRACTIn this paper, we propose a novel intrusion detection approach using past driving experience and the neural knowledge DNA for in-vehicle information system security. The neural knowledge DNA is a novel knowledge representation method designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing systems. We examine our approach for classifying malicious vehicle control commands based on learning from past valid driving behavior data on a simulator.

A Novel Driver Performance Model Based on Machine Learning

Models of road vehicle driver behaviour are widely used in several disciplines, like driver distraction and autonomous driving. In this paper, a novel driver performance model, which is unique for every driver, is introduced. The driver is modelled with machine learning algorithms, namely artificial neural network and adaptive neuro-fuzzy inference system. Every model is trained and validated with the data collected during the real-time driver-in-the-loop experiment on a vehicle simulator for each driver separately. In total, 18 participants contributed to the experiment. Although the prediction accuracy of the models depends on the algorithm specifications, the artificial neural network was slightly more accurate in driver performance prediction comparing to the adaptive neuro-fuzzy inference system. The driver models may be used in detection of driver distraction induced by in-vehicle information system.

Usability Study of Two In-Vehicle Information Systems Using Finger Tracking and Facial Expression Recognition Technology

ABSTRACTIn this study, an experiment is conducted to compare the usability of two in-vehicle information systems (IVISs), namely, the newly developed “Calink” and the commercially available “Carlife.” The interface style of Calink is similar to that of smartphones, while that of Carlife is similar to the interface style of traditional IVISs. This study establishes a usability index system based on the International Organization for Standardization’s definition of usability, and each evaluation index has a corresponding subjective or objective measurement method. Five new indexes, namely, angular velocity, velocity, turn angle, distance of finger movements, and valence and arousal value of facial expression, are used in the usability test. Participants are asked to complete three tasks (namely, music, telephone, and navigation) to test the usability of the IVIS interface. The results show that in the case of most of the indexes Calink performs better than Carlife. Finally, the consistency of subjective experience and objective performance is discussed.

Personalized Driver Workload Inference by Learning From Vehicle Related Measurements

Adapting in-vehicle systems (e.g., advanced driver assistance systems and in-vehicle information systems) to individual drivers’ workload can enhance both safety and convenience. To make this possible, it is a prerequisite to infer driver workload so that adaptive aiding can be provided to the driver at the right time and in an appropriate manner. Rather than developing an average model for all drivers, a personalized driver workload inference (PDWI) system considering individual drivers driving characteristics is developed using machine learning techniques via easily accessed vehicle related measurements (VRMs). The proposed PDWI system comprises two stages. In offline training, individual drivers workload is first automatically splitted into different categories according to its inherent data characteristics using fuzzy C-means (FCM) clustering. Then an implicit mapping between VRMs and different levels of workload is constructed via classification algorithms. In online implementation, VRMs samples are classified into different clusters, consequently driver workload type can be successfully inferred. A recently collected dataset from real-world naturalistic driving experiments is drawn to validate the proposed PDWI system. Comparative experimental results indicate that the proposed framework integrating FCM clustering and support vector machine classifier provides a promising workload recognition performance in terms of accuracy, precision, recall, ${F_{1}}$ -score, and prediction time. The interindividual differences in term of workload are also identified and can be accommodated by the proposed framework due to its adaptiveness.

Using tactile detection response tasks to assess in-vehicle voice control interactions

In-Vehicle Information Systems (IVIS) are becoming more accessible to drivers but also contain more complex communication features. Voice control systems are shown to be less distracting than visual-manual interfaces, but they can still impose cognitive workload. This study examined the cognitive workload associated with interactions with voice systems while driving. Cognitive workload was assessed using a Tactile Detection Response Task (TDRT) protocol. A driving simulator study with 48 participants was conducted using an interface with a Wizard-of-Oz based voice control system. Drivers conducted several voice tasks that included radio channel selection, address navigation, and scheduling a calendar appointment. Recognition accuracy and system delay time was manipulated within these voice tasks. Using a mixed linear model, cognitive workload was shown to be higher for navigation and calendar tasks when compared to radio tasks. Recognition errors and time delays in the calendar task significantly decreased TDRT response time. Drivers that are distracted by voice control systems that also contained system delays and errors do not necessarily increase TDRT response time. In fact, drivers may adapt over time to these system imperfections.

Driver adaptation to lane keeping assistance systems: Do drivers become less vigilant?

Driver adaptation to semi-autonomous vehicles is examined in this study using a longitudinal driving simulator study with 18 subjects randomly assigned to a control (n = 9) and treatment (n = 9) group. A Tactile Detection Response Task (TDRT) was used to measure cognitive workload while drivers engaged in visual-manual distracting tasks using an in-vehicle information system, with and without a lane-keeping assistance system (baseline, intervention, and withdrawal period). Measures of miss rate and reaction time to the TDRT stimuli suggest that drivers in both groups showed decreases in cognitive workload as they gained experience. Participants in the treatment group experienced higher cognitive workload when the lane keeping assistance system was withdrawn. On average, there were greater decreases in cognitive workload over time for the difficult tasks as compared to the easy tasks for both the control and treatment groups. The effect was more pronounced in the control group with larger decreases in cognitive workload. The NASA TLX was also comparable to the task accuracy. These results suggest that adaptations in driver behavior due to exposure to automation may have transfer effects on cognitive workload and thus may lead to safety implications.

Effects of interface layout on the usability of In-Vehicle Information Systems and driving safety

This study investigated the impact of two In-Vehicle Information Systems (IVISs) on both usability and driving safety. We simulated vehicle checkerboard and hierarchical IVIS interface layouts and examined their usability based on task completion time, error rate, NASA-TLX, and System Usability Scale (SUS). Our driving safety measures included mean glance time, mean number of glances, blink frequency, and heart rate while real driving. The driving performances of 24 participants were recorded and assessed in real driving tasks of four driving speeds (0, 30, 60, and 80km/h). Our results show that task completion time, SUS, and mean number of glances were significantly different with the two layouts. Moreover, we observed that the two layouts varied in their impacts on usability and driving safety at the different speeds. The hierarchical layout was more efficient at the lower speeds of 0 and 30km/h, while the checkerboard layout outperformed at the higher speeds of 60 and 80km/h. This study offers empirical evidence for IVIS interface design.

So what really is user experience? An experimental study of user needs and emotional responses as underlying constructs

This driving simulator study extended knowledge on user experience using a strategy to mitigate distraction resulting from the use of in-vehicle information systems (IVISs). It examined the impact of system restrictions on users’ needs, emotions and consequences of users’ experience in terms of psychological reactance. In a repeated measures design, we asked 53 participants to perform secondary tasks with an IVIS while driving. Three versions of the system varied with respect to the number of operable functionalities. The more functionalities that were disabled while driving, the more negatively users rated the systems. Multilevel regression analyses of at least n = 155 data points revealed that drivers’ need fulfilment predicted their emotions. Reactance depended on users’ need fulfilment and emotions. Experienced autonomy mediated the relation between functional limitations and reactance. When developing interactive systems, one should focus on needs and be aware of potential unwanted consequences such as psychological reactance.Practitioner Summary: This driving simulator study highlights the importance of considering need fulfilment and users’ emotions when developing an interactive system that provides high user experience. System restrictions could have negative consequences as users might show psychological reactance.

G4-1 A Study of Multi-touch Screen Installation in Vehicles for Single-touch and Gestural Operations

Touchscreens have been used for human-machine interactions in vehicle for years; researchers paid more attention to using touchscreen displays when driving was the primary task and the driving performance was the main concern. However, it is not legal to operate touchscreen devices while driving in most of modern countries. On the other hand, a driver may occasionally control in-vehicle systems through touch or even gestural operations on the touch display. The situation is becoming more and more popular and worthwhile further study. The current study investigated three different installations of a multi-touch screen in vehicle at high (around the driver's eye height in sitting), middle (around the height of the driver's shoulder) and low (around the height of the driver's waist) positions. Twenty-seven voluntary participants (aged 23.93±0.68 years old) were recruited to perform single-touch and gestural operations in a simulated car cabin. Their performance in accuracy for the two experimental tasks and subjective responses regarding difficulty, fatigue and preferences of the task and interfaces were recorded. The results of the study showed that, regardless of the single-touch target clicking task or the multitouch target fitting task, the averaged errors over all target positions were the least for the middle installation and were the highest for the low installation. When user performance for different target sizes was compared in the gestural operations, participants tended to deviate more when the target size was small (100-pixel diameter) but manipulated the size more accurately when it was big (200-pixel diameter). The subjective evaluation through questionnaires showed that participants preferred the middle installation. The high installation was considered more difficult and caused greater fatigue on right neck, shoulder, upper extremities, wrist, and thumb belly areas. Based on the results of the current study, the middle installation is recommended for its higher accuracy and less fatigue. The upper installation, despite being recommended by other literature, caused greater errors, higher difficulty and fatigue, resulting the least preference. The difficulty and fatigue was not revealed for the lower installation, but it caused lower accuracy in gestural operations. This study could contribute to future design of in-vehicle touch interfaces in that the accuracy of single-touch and multi-touch tasks may be improved by installing the touchscreen at a middle height (around shoulder level). Future studies may consider verifying experiments in an interface simulating real in-vehicle information systems and increasing the number of data collected so that the result can be more applicable in reality.

A Generalized Method to Extract Visual Time-Sharing Sequences From Naturalistic Driving Data

Indicators based on visual time-sharing have been used to investigate drivers’ visual behaviour during additional task execution. However, visual time-sharing analyses have been restricted to additional tasks with well-defined temporal start and end points and a dedicated visual target area. We introduce a method to automatically extract visual time-sharing sequences directly from eye tracking data. This facilitates investigations of systems, providing continuous information without well-defined start and end points. Furthermore, it becomes possible to investigate time-sharing behavior with other types of glance targets such as the mirrors. Time-sharing sequences are here extracted based on between-glance durations. If glances to a particular target are separated by less than a time-based threshold value, we assume that they belong to the same information intake event. Our results indicate that a 4-s threshold is appropriate. Examples derived from 12 drivers (about 100 hours of eye tracking data), collected in an on-road investigation of an in-vehicle information system, are provided to illustrate sequence-based analyses. This includes the possibility to investigate human-machine interface designs based on the number of glances in the extracted sequences, and to increase the legibility of transition matrices by deriving them from time-sharing sequences instead of single glances. More object-oriented glance behavior analyses, based on additional sensor and information fusion, are identified as the next future step. This would enable automated extraction of time-sharing sequences not only for targets fixed in the vehicle’s coordinate system, but also for environmental and traffic targets that move independently of the driver’s vehicle.

Talking to your car can drive you to distraction.

This research examined the impact of in-vehicle information system (IVIS) interactions on the driver’s cognitive workload; 257 subjects participated in a weeklong evaluation of the IVIS interaction in one of ten different model-year 2015 automobiles. After an initial assessment of the cognitive workload associated with using the IVIS, participants took the vehicle home for 5 days and practiced using the system. At the end of the 5 days of practice, participants returned and the workload of these IVIS interactions was reassessed. The cognitive workload was found to be moderate to high, averaging 3.34 on a 5-point scale and ranged from 2.37 to 4.57. The workload was associated with the intuitiveness and complexity of the system and the time it took participants to complete the interaction. The workload experienced by older drivers was significantly greater than that experienced by younger drivers performing the same operations. Practice did not eliminate the interference from IVIS interactions. In fact, IVIS interactions that were difficult on the first day were still relatively difficult to perform after a week of practice. Finally, there were long-lasting residual costs after the IVIS interactions had terminated. The higher levels of workload should serve as a caution that these voice-based interactions can be cognitively demanding and ought not to be used indiscriminately while operating a motor vehicle.