Design Of In-vehicle Information Systems Research Articles

Evaluation of driver demand for in-vehicle information: An integrated method combining clustering and multivariate ordered probit model

Introduction: The proper execution of driving tasks requires information support. While new technologies have increased the convenience of information access, they have also increased the risk of driver distraction and information overload. Meeting drivers’ demands and providing them with adequate information are crucial to driving safety. Methods: Based on a sample of 1,060 questionnaires, research on driving information demands is conducted from the perspective of drivers. A principal component analysis and the entropy method are integrated to quantify the driving information demands and preferences of drivers. The K-means classification algorithm is selected to classify the different types of driving information demands, including dynamic traffic information demands (DTIDs), static traffic information demands (STIDs), automotive driving status information demands (ATIDs), and total driving information demands (TDIDs). Fisher's least significant difference (LSD) is used to compare the differences in the numbers of self-reported crashes among different driving information demand levels. A multivariate ordered probit model is established to explore the potential factors that influence the different types of driving information demand levels. Results: The DTID is the driver's most in-demand information type, and accordingly, gender, driving experience, average driving mileage, driving skills, and driving style significantly affect the driving information demand levels. Moreover, the number of self-reported crashes decreased as the DTID, ATID, and TDID levels decreased. Conclusion: Driving information demands are affected by a variety of factors. This study also provides evidence that drivers who have higher driving information demands are more likely to drive more carefully and safely than their counterparts who do not exhibit high driving information demands. Practical implications: The results are indicative of the driver-oriented design of in-vehicle information systems and the development of dynamic information services as a way to avoid negative impacts on driving.

Event-related driver stress detection with smartphones among young novice drivers

Complex and diverse driving situations can pose short-term stressors to novice drivers. Continuously detecting stress is essential for driver training, stress intervention, and the design of in-vehicle information systems. This study designed and validated a driver stress detection method at the event level based on machine learning algorithms and facial features captured with smartphones. Thirty young novice drivers completed two driving tasks containing eight events of two versions (neutral and stressful), with psychological, physiological, and facial data collected. Four combinations of input data types and six machine learning algorithms were used to detect stressful events. The KNN algorithm with facial plus individual profile features yielded the highest accuracy of 89.2%. Adding individual profile features can improve classification performance. Facial areas such as brow, eye, jaw, nose, and mouth were most sensitive to stress. This approach could provide more temporal-spatial information about the driver’s stress levels during the whole driving process. Practitioner Summary: This paper proposed a method to detect driver stress at the event level with smartphones. Models with facial plus individual profile features and the KNN algorithm had the most outstanding classification performance. The presented approach can serve as a tool for improving in-vehicle interaction system design when considering driver stress. Abbreviations: GSR: galvanic skin response; ECG: electrocardiography; HR: heart rate; HRV: heart rate variability; RGB: red green blue; NIR: near-infrared; IP: individual profile; DSI: driver stress inventory; APS: arousal predisposition scale; API: application programming interface; PPG: photoplethysmography; EDR: electrodermal response; PD: pupil diameter; SCL: skin conductance level; RF: random forest; KNN: k-nearest neighbour; LDA: linear discriminant analysis; QDA: quadratic discriminant analysis; SVML: support vector machines with the linear kernel; SVMP: support vector machines with the polynomial kernel; TP: true positive; TN: true negative; FP: false positive; FN: false negative; t-SNE: t-distributed stochastic neighbour embedding

Cross-regional in-vehicle information system design: the preferences and comprehension of Australian, US and Chinese drivers

The increasing global distribution of automobiles necessitates that the design of in-vehicle information systems (IVIS) is appropriate for the regions to which they are being exported. Differences between regions such as culture, language, environment and traffic context can have important influences on the usability and acceptance of IVIS and, as a consequence, on driving performance and safety. This pilot study examined the cross-regional factors that may influence IVIS preferences and comprehension, across three regions: Australia, China and the United States. The study used an online questionnaire to examine drivers' preferences for, and comprehension of, a range of surface-level aspects of IVIS interfaces. Cross-regional factors including cultural values, demographic and driving pattern data were also collected. The study was conducted locally within Australia, employing a sample of recently migrated American and Chinese and local Australian students. Regional differences were found in terms of current technology use, preferences for IVIS input control types and labels, and in the recognition and comprehension of IVIS functions and features. These findings are discussed within the framework of relevant regional differences. Based on the findings, a number of general and region-specific IVIS design recommendations are offered.

The implications of cross-regional differences for the design of In-vehicle Information Systems: A comparison of Australian and Chinese drivers

The increasing global distribution of automobiles necessitates that the design of In-vehicle Information Systems (IVIS) is appropriate for the regions to which they are being exported. Differences between regions such as culture, environment and traffic context can influence the needs, usability and acceptance of IVIS. This paper describes two studies aimed at identifying regional differences in IVIS design needs and preferences across drivers from Australia and China to determine the impact of any differences on IVIS design. Using a questionnaire and interaction clinics, the influence of cultural values and driving patterns on drivers' preferences for, and comprehension of, surface- and interaction-level aspects of IVIS interfaces was explored. Similarities and differences were found between the two regional groups in terms of preferences for IVIS input control types and labels and in the comprehension of IVIS functions. Specifically, Chinese drivers preferred symbols and Chinese characters over English words and were less successful (compared to Australians) at comprehending English abbreviations, particularly for complex IVIS functions. Implications in terms of the current trend to introduce Western-styled interfaces into other regions with little or no adaptation are discussed.

Effects of Voice Technology on Test Track Driving Performance: Implications for Driver Distraction

This work compares the degradation in driving performance associated with secondary tasks performed with voice-based and visual/manual interfaces, including radio tuning, phone dialing, and more complex tasks involving a sequence of interactions with an in-vehicle computer system. Twenty-one participants drove an instrumented vehicle while performing a combination of car-following, peripheral target detection, and secondary tasks on a closed test track. Drivers compensated for increased task demands associated with secondary tasks by increasing their following distance. Performing secondary tasks also resulted in significant decrements to vehicle control, target detection, and car-following performance. The voice-based interface helped reduce the distracting effects of secondary task performance. Modest improvements were observed for measures of vehicle control and target detection but not for car following. The results indicated that performing in-vehicle tasks required diversion of both peripheral (visual and manual) and attentional (cognitive) resources from driving. The voice-based interface reduced the peripheral impairment but did not appreciably reduce the attentional impairment. Actual or potential applications of this research include improvements to the design of invehicle information systems and the development of evaluation protocols to assess their distraction potential.

Speech-based interaction with in-vehicle computers: the effect of speech-based e-mail on drivers' attention to the roadway.

As computer applications for cars emerge, a speech-based interface offers an appealing alternative to the visually demanding direct manipulation interface. However, speech-based systems may pose cognitive demands that could undermine driving safety. This study used a car-following task to evaluate how a speech-based e-mail system affects drivers' response to the periodic braking of a lead vehicle. The study included 24 drivers between the ages of 18 and 24 years. A baseline condition with no e-mail system was compared with a simple and a complex e-mail system in both simple and complex driving environments. The results show a 30% (310 ms) increase in reaction time when the speech-based system is used. Subjective workload ratings and probe questions also indicate that speech-based interaction introduces a significant cognitive load, which was highest for the complex e-mail system. These data show that a speech-based interface is not a panacea that eliminates the potential distraction of in-vehicle computers. Actual or potential applications of this research include design of in-vehicle information systems and evaluation of their contributions to driver distraction.

IVIS DEMAnD: A Prototype Software Package to Aid in the Design of In-Vehicle Information Systems

IVIS DEMAnD: A Prototype Software Package to Aid in the Design of In-Vehicle Information Systems

Travelers' preferences for in-vehicle information systems: An exploratory analysis

A sample of travelers' preferences toward in-vehicle traffic information systems was undertaken and appropriate statistical models were estimated. Specifically, ordered logit and regression analyses were conducted to quantify travelers' ratings of the importance of in-vehicle system attributes, and the distance ahead that they prefer to be notified of various types of information provided by in-vehicle systems. Model estimation results show that travelers' socio-economics, habitual travel patterns, commute congestion levels and attitudes toward in-vehicle technologies are significant determinants of travelers' importance ratings and the preferred distance ahead of in-vehicle system information. These model results provide important information for both marketing and design of in-vehicle information systems.