In-vehicle Information Systems Research Articles

The Effect of Touchscreen Layout Division on the Usability and Driving Safety of In-Vehicle Information Systems

Compared to traditional physical controls, in-vehicle touchscreen interaction relies more on vision. Irrational In-Vehicle Information System (IVIS) interface layout design can lead to decreased driver performance and even serious traffic accidents. This study investigated the impact of IVIS layouts on usability and driving safety. We extracted and analyzed the current mainstream IVIS homepage layouts in the market and summarized four different layout structures. An experiment was conducted on a driving simulator to examine the usability of the IVIS layout design based on task reaction time, error rate, NASA-TLX (a subjective workload assessment scale developed by NASA), and subjective preference (PREF). The safety issues of IVIS layout design were also examined based on standard deviation lane position (SDLP), speed variation (SV), mean glance time, mean number of glances, and blink frequency. This study recorded and evaluated the driving performance of 40 participants during real driving tasks at four speeds (0, 60, 90, and 120 km/h). Results showed that there were significant differences in task reaction time, NASA-TLX, PREF, mean glance time, SDLP, and SV among the four layouts. Furthermore, we observed that these four layouts had varying effects on usability and driving safety at four speeds. In the stationary state, the three-partition layouts were more efficient, while the two-partition layouts had more advantages during driving. Meanwhile, the horizontal dock bar layout interfaces have more advantages compared to the vertical dock bar interface. The study provides empirical evidence for optimizing the IVIS interface layout design for enhanced usability and driving safety.

Predicting User Preference for Innovative Features in Intelligent Connected Vehicles from a Cultural Perspective

The increasing level of intelligence in automobiles is driving a shift in the human–machine relationship. Users are paying more attention to the intelligent cabin and showing a tendency toward customization. As culture is considered to be an important factor in guiding user behavior and preference, this study innovatively incorporates cultural and human factors into the model to understand how individual cultural orientation influences user preference for innovative human-machine interaction (HMI) features. Firstly, this study considered five Hofstede cultural dimensions as potential impact factors and constructed a prediction model through the random forest algorithm so as to analyze the influence mechanism of culture. Subsequently, K-means clustering was used to classify the sample into three user groups and then predict their preferences for the innovative features in the intelligent cabin. The results showed that users with a higher power distance index preferred a sense of ceremony and show-off-related features such as ambient lighting and welcome mode, whereas users with high individualism were keen on a more open and personalized in-vehicle information system. Long-term orientation was found to be associated with features that help to improve efficiency, and users with a lower level of uncertainty avoidance and restraint were more likely to be attracted to new features and were also more willing to use entertainment-related features. The methodology developed in this study can be widely applied to people in different countries, thus effectively exploring the personal requirements of different individuals, guiding further user experience design and localization when breaking into a new market.

The effect of in‐vehicle agent embodiment on drivers' perceived usability and cognitive workload: Evidence from subjective reporting, ECG, and fNIRS

AbstractTo improve the interaction between drivers and the in‐vehicle information system (IVIS), various intelligent agents, such as robot agents, virtual agents, and voice‐only agents, have been integrated into vehicles. However, it is not yet clear which type of in‐vehicle agent is best suited to the driving context. This study aims to investigate the effect of in‐vehicle agent embodiment on drivers' perceived usability and cognitive workload. In a within‐subject simulated driving experiment, 22 participants interacted with three different in‐vehicle agents (smartphone agent, robot agent, and virtual agent). Functional near‐infrared spectroscopy and electrocardiogram (ECG) were used to record prefrontal cortex activation and electrical changes associated with cardiac activity during simulated driving, respectively. The results show that the smartphone agent had the lowest perceived usability scores, oxygenated hemoglobin concentration variation (ΔHbO), and maximum ECG signal variation compared to baseline. There were no statistical differences in cognitive workload, perceived usability scores, brain area activation, and ECG signals between the robot agent and the virtual agent. The research findings demonstrate the positive effects of the anthropomorphic appearance of in‐vehicle agents on perceived usability and contribute to improving the design of in‐vehicle intelligent agents.

A novel FUZZY-FMEA method for risk assessment of In-vehicle information system under uncertainty

A novel FUZZY-FMEA method for risk assessment of In-vehicle information system under uncertainty

Distraction-level recognition based on stacking ensemble learning for IVIS secondary tasks

Driver distraction-level recognition while performing secondary tasks in full-touch in-vehicle information systems (FTIVISs) is essential for the harmonious co-driving of human and intelligent vehicle systems. However, there has been little research on this topic. To respond to this issue, this paper proposes a distraction-level recognition framework with a combination of semi-supervised learning, unsupervised learning, and supervised learning. First, unsupervised learning and semi-supervised is used to divide the collected unlabeled samples of driving distraction behavior into three categories of distraction levels: high, medium, and low. Second, the factors influencing the distraction level are explored through a mixed model analysis. Finally, a stacking-based ensemble learning model is proposed to recognize the driver distraction level by supervised learning, with the influencing factors of the distraction level used as model input parameters. To improve the recognition performance of the stacking model, four heterogeneous classifiers selected as the base classifiers, and principal component analysis (PCA) is introduced in the base classifier layer, which improves the ability of the meta-classifier to process raw features based on overfitting resistance. We conducted a real road experiment under different road and FTIVIS task conditions, and the proposed model performed better than traditional machine learning models. In addition, the model exhibited the greatest advantage when the deep neural network (DNN) algorithm was used as the meta-classifier of the model, with a recognition accuracy of 92.5%. The study findings are significant for developing a human–machine co-driving control strategy and improving vehicle driving safety.

Does in-vehicle automation help individuals with Parkinson's disease? A preliminary analysis.

PD is a progressive neurodegenerative disorder that affects, according to the ICF, body systems (cognitive, visual, and motor), and functions (e.g., decreased executive functions, decreased visual acuity, impaired contrast sensitivity, decreased coordination)-all which impact driving performance, an instrumental activity of daily living in the domain of "Activity" and "Participation" according to the ICF. Although there is strong evidence of impaired driving performance in PD, few studies have explored the real-world benefits of in-vehicle automation technologies, such as in-vehicle information systems (IVIS) and advanced driver assistance systems (ADAS), for drivers with PD. These technologies hold potential to alleviate driving impairments, reduce errors, and improve overall performance, allowing individuals with PD to maintain their mobility and independence more safely and for longer periods. This preliminary study aimed to fill the gap in the literature by examining the impact of IVIS and ADAS on driving safety, as indicated by the number of driving errors made by people with PD in an on-road study. Forty-five adults with diagnosed PD drove a 2019 Toyota Camry equipped with IVIS and ADAS features (Toyota Safety Sense 2.0) on a route containing highway and suburban roads. Participants drove half of the route with the IVIS and ADAS systems activated and the other half with the systems deactivated. The results suggest that systems that assume control of the driving task, such as adaptive cruise control, were most effective in reducing driving errors. Furthermore, individual differences in cognitive abilities, particularly memory, were significantly correlated with the total number of driving errors when the systems were deactivated, but no significant correlations were present when the systems were activated. Physical capability factors, such as rigidity and bradykinesia, were not significantly correlated with driving error. Taken together, these results show that in-vehicle driver automation systems can benefit drivers with PD and diminish the impact of individual differences in driver cognitive ability.

Comparison of the Box Task combined with a tactile Detection Response Task and the Occlusion Method as measures of secondary task demand

In-vehicle information systems (IVIS) demand driver attention and lead to driver distraction and inattention, impairing driving safety. The Box Task (BT) quantifies these secondary task demands concerning visual and manual aspects of the driving task. Combined with the tactile Detection Response Task (tDRT), it also measures cognitive demands. The standard method for assessing secondary task demand is the occlusion method. The present study compares the combined BT+tDRT method concerning various secondary task demands with the standardized occlusion method. In a laboratory study, participants completed artificial and realistic IVIS tasks with BT+tDRT or occlusion. Results revealed that BT+tDRT was able to distinguish different demands of visual, manual, and cognitive tasks, while the occlusion method was only able to distinguish different visual-manual IVIS tasks. Based on the results, we conclude that the combination of BT+tDRT captures the effects of IVIS on driving safety more precisely and comprehensively than does the occlusion method. Future research should compare the findings from this study with data from field studies. Also, the use of BT+tDRT should be further standardized.

A novel autonomous vehicle interface for older adults with cognitive impairment

The population of older Americans with cognitive impairments, especially memory loss, is growing. Autonomous vehicles (AVs) have the potential to improve the mobility of older adults with cognitive impairment; however, there are still concerns regarding AVs’ usability and accessibility in this population. Study objectives were to (1) better understand the needs and requirements of older adults with mild and moderate cognitive impairments regarding AVs, and (2) create a prototype for a holistic, user-friendly interface for AV interactions. An initial (Generation 1) prototype was designed based on the literature and usability principles. Based on the findings of phone interviews and focus group meetings with older adults and caregivers (n = 23), an enhanced interface (Generation 2) was developed. This generation 2 prototype has the potential to reduce the mental workload and anxiety of older adults in their interactions with AVs and can inform the design of future in-vehicle information systems for older adults.

Multitasking While Driving: How Drivers Self-Regulate Their Interaction with In-Vehicle Touchscreens in Automated Driving

Driver assistance systems are designed to increase comfort and safety by automating parts of the driving task. At the same time, modern in-vehicle information systems with large touchscreens provide the driver with numerous options for entertainment, information, or communication, and are a potential source of distraction. However, little is known about how driving automation affects how drivers interact with the center stack touchscreen, i.e., how drivers self-regulate their behavior in response to different levels of driving automation. To investigate this, we apply multilevel models to a real-world driving dataset consisting of 31,378 sequences. Our results show significant differences in drivers’ interaction and glance behavior in response to different levels of driving automation, vehicle speed, and road curvature. During automated driving, drivers perform more interactions per touchscreen sequence and increase the time spent looking at the center stack touchscreen. Specifically, at higher levels of driving automation (level 2), the mean glance duration toward the center stack touchscreen increases by 36% and the mean number of interactions per sequence increases by 17% compared to manual driving. Furthermore, partially automated driving has a strong impact on the use of more complex UI elements (e.g., maps) and touch gestures (e.g., multitouch). We also show that the effect of driving automation on drivers’ self-regulation is greater than that of vehicle speed and road curvature. The derived knowledge can inform the design and evaluation of touch-based infotainment systems and the development of context-aware driver monitoring systems.

AN IOT BASED SMART E-FUEL STATION USING ESP-32

IoT and Cloud computing is becoming more in demand now days due to advancement in its technology. Most of the fuel stations these days are operated manually which are time consuming and require more manpower. It is very difficult and costly to provide excellent facility to the consumers in a fuel station present in a distant area. All these problems can be solved by the use of the proposed project. This paper presents the design and implementation of a smart e-fuel station using IoT technology in which the system is capable of deducting the amount of petrol dispensed from a user-based card using RFID technology and measure the level of fuel in the station and upload it to the cloud server. This cloud server is only accessible to the admin. If the fuel level is low then the main station will provide the fuel to that station. Everything is getting automated in today’s world.
 Existing system:
 In previous we have seen in fuel station to fill the fuel we need human help needed for filling the fuel and there is no proper protection in fuel stations like fire sensor, smoke sensor etc. this lead to damage and also fire accidents.
 
 Proposed system:
 The system proposed describes the creation and management of a smart fuel. An interactive, digital system is designed to improve user interface.
 It uses a data procurement system based on the microcontroller that uses AC to bring control data from analog to digital format. Tough the in-vehicle information systems are spread throughout a practical vehicle’s body, a communication module is adopted which supports the incorporation of a controlled fuel dispensing system for vehicles through an ESP32 microcontroller-controlled device.
 In this work the Automatic vehicle identification (AVI) system is adopted and further the customer will scan his RFID/Wi-Fi card with all the details installed in it.

Impact of In-Air Gestures on In-Car Task’s Diver Distraction

As in-vehicle information systems (IVIS) grow increasingly complex, the demand for innovative artificial intelligence-based interaction methods that enhance cybersecurity becomes more crucial. In-air gestures offer a promising solution due to their intuitiveness and individual uniqueness, potentially improving security in human–computer interactions. However, the impact of in-air gestures on driver distraction during in-vehicle tasks and the scarcity of skeleton-based in-air gesture recognition methods in IVIS remain largely unexplored. To address these challenges, we developed a skeleton-based framework specifically tailored for IVIS that recognizes in-air gestures, classifying them as static or dynamic. Our gesture model, tested on the large-scale AUTSL dataset, demonstrates accuracy comparable to state-of-the-art methods and increased efficiency on mobile devices. In comparative experiments between in-air gestures and touch interactions within a driving simulation environment, we established an evaluation system to assess the driver’s attention level during driving. Our findings indicate that in-air gestures provide a more efficient and less distracting interaction solution for IVIS in multi-goal driving environments, significantly improving driving performance by 65%. The proposed framework can serve as a valuable tool for designing future in-air gesture-based interfaces for IVIS, contributing to enhanced cybersecurity.

Effects of Road Type and IVIS Task Type on Driver Behavior and Driving Performance in Agricultural Tractors

In recent years, terrible driver behavior and driving performance have been the primary causes of increasing agricultural tractor accidents. However, how environmental factors influence driver behavior and driving safety is not clarified clearly yet for tractors. Therefore, this article researched the effects of road type and in-vehicle information system (IVIS) task type on driver behavior and driving performance. Thirty-two participants were randomly assigned to four groups equally. They finished the simulation on three types of roads while each group conducted one different IVIS task. Drivers’ wrist movement acceleration (ACC), rotation angular velocity (GYRO), and surface temperature (TEMP) were recorded and statistically analyzed together with the driving performance parameter (DP) and workload index. Results showed that road and IVIS task types significantly affected drivers’ ACC, GYRO, TEMP, and DP. IVIS task type significantly affected driver workload. This article will contribute to road and tractor IVIS design, driver behavior and safety research.

Exploring the factors influencing acquisition and learning experiences of cars fitted with advanced driver assistance systems (ADAS)

Advanced Driver Assistance Systems (ADAS) have shown substantial potential to increase road safety. To guarantee a growing presence and correct use of ADAS, road safety initiatives such as driver education and programs to promote safer vehicles need to be oriented towards the specific needs of individual drivers. The aim of this study was to explore a sample of Australian drivers’ rationale for driving a vehicle with ADAS and the strategies that they use when learning to operate the functions of ADAS. Semi-structured interviews were conducted with 48 Australian drivers aged between 19 and 78 years (M = 41.54, SD = 16.32; 25 females). The results were organised into four themes: (i) reasons for driving/purchasing a car with ADAS, (ii) systems influencing driver purchasing systems, (iii) strategies used by drivers when learning about ADAS, and (iv) perceptions regarding the importance of learning and training to use ADAS. Acquisition of vehicles with ADAS frequently occurred because of the perceived safety benefits of specific systems (e.g., rear parking sensor or in-vehicle information system). Additionally, some participants did not highlight the safety benefits as the main reason for driving/purchasing a car with ADAS. Indeed, some participants mentioned that ADAS acquisition was not a reasoned process but rather an opportunistic decision when upgrading their vehicles, mainly because these systems were already included in a vehicle. Regarding education, it was found that participants were using trial-and-error as their main approach to learning about the capabilities of ADAS and official sources of knowledge about the system were rarely consulted. On a positive note, participants recognised the importance of learning to use ADAS systems correctly, showing that there is demand for effective and engaging driver education.

Testing Road Vehicle User Interfaces Concerning the Driver’s Cognitive Load

This paper investigates the usability of touch screens used in mass production road vehicles. Our goal is to provide a detailed comparison of conventional physical buttons and capacitive touch screens taking the human factor into account. The pilot test focuses on a specific Non-driving Related Task (NDRT): the control of the on-board climate system using a touch screen panel versus rotating knobs and push buttons. Psychological parameters, functionality, usability and, the ergonomics of In-Vehicle Information Systems (IVIS) were evaluated using a specific questionnaire, a system usability scale (SUS), workload assessment (NASA-TLX), and a physiological sensor system. The measurements are based on a wearable eye-tracker that provides fixation points of the driver’s gaze in order to detect distraction. The closed road used for the naturalistic driving study was provided by the ZalaZONE Test Track, Zalaegerszeg, Hungary. Objective and subjective results of the pilot study indicate that the control of touch screen panels causes higher visual, manual, and cognitive distraction than the use of physical buttons. The statistical analysis demonstrated that conventional techniques need to be complemented in order to better represent human behavior differences.

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.

Does the AR-HUD system affect driving behaviour? An eye-tracking experiment study

In recent years, the continuous development of in-vehicle information systems (IVIS) has greatly enriched the driving experience, while also occupying the driver’s cognitive resources to varying degrees, causing driving distraction. Therefore, studying the influence of the AR-HUD (Augmented Reality Head-up Display, AR-HUD) system on driving behaviour is of great significance for the in-vehicle information system to manage the complexity of information and enhance driving safety. This experiment applied five typical risky driving scenarios under two lighting conditions (daytime and night), as stimulus materials. The effects of eye movement behaviour and risk reaction time indicators during driving were comprehensively analysed. The experiment results showed that the AR-HUD system can significantly improve the subjects' attention to risky AOIs (Area of Interest, AOI) in night driving situations, as well as reduce the difficulty of processing information in risky driving scenarios, thus reducing cognitive load; In terms of reaction time, the AR-HUD system can significantly reduce the driver's perception time for risky driving scenarios, thus responding to risky situations more quickly. The experiment conclusions verified the role of AR-HUD technology in improving driving safety and driving behaviour, and provide a new direction for the future development of in-vehicle information systems.

Input modality matters: A comparison of touch, speech, and gesture based in-vehicle interaction

Innovative input devices are being available for in-vehicle information systems (IVISs). While they have the potential to provide enjoyable driving by enabling drivers to perform non-driving related tasks (NDRTs) in more natural ways, the associated distracting effects should be paid with more attention. The purpose of this exploratory study was to compare the effects of three novel input modalities, i.e., touchscreen-based interaction (TBI), speech-based interaction (SBI), and gesture-based interaction (GBI), on driving performance and driver visual behaviors. Moreover, we examined if the influence of different modalities would be moderated by the difficulty level of NDRTs. A total of 36 participants were invited to a simulated driving experiment where they were randomly assigned to one of the four groups (TBI, GBI, SBI or baseline) and completed three driving trials. The results showed that TBI led to the worse driving performance, as indicated by the significantly prolonged reaction time, reduced minimum time-to-collision, and increased variations in both longitudinal and lateral vehicle control. The deteriorated driving performance could be attributed, at least partially, to the intense visual demand induced by looking towards the touchscreen, as indicated by more and longer off-the-road glances. The adverse impacts of GBI were relatively smaller, but it still posed great crash risk by leading to a shorter minimum time-to-collision and less stable vehicle control compared to the baseline. SBI, although not completely equivalent to the baseline group, showed the minimum influence on driving and visual performance. Only very few interaction effects were found, suggesting that the effects of modality were quite robust across different NDRTs. It was concluded that SBI and GBI provided safer alternatives to in-vehicle interaction than TBI.

Real-Time Multistep Time-Series Prediction of Driver’s Head Pose During IVIS Secondary Tasks for Human–Machine Codriving and Distraction Warning Systems

An accurate prediction of a driver’s head pose in the in-vehicle information system (IVIS) tasks is essential for both distraction warning and vehicle takeover rule-making in human–machine codriving. In this study, a real-world experiment is conducted to collect data on driver behavior and vehicle state during the IVIS tasks under different road conditions. An innovative scheme is proposed for multistep time-series prediction of a driver’s head pose. The proposed scheme consists of two main parts: a data-processing module and a model module. Furthermore, a combination of a machine-learning-based method and a geometric method is used to estimate a driver’s head pose, and the MediaPipe model is used to extract the hand data. In the model module, a convolutional neural network (CNN), which can automatically capture short-term local dependencies in the head pose data, and a bidirectional long short-term memory (BiLSTM), which can capture long-term macro dependencies, are combined. In addition, an attention mechanism is used to weigh important time periods and variables. Finally, the performance of the proposed hybrid CNN-BiLSTM-attention model in the head pose prediction is validated, and the proposed model is compared with three traditional machine-learning-based models and the CNN-BiLSTM model. The comparison results show that the proposed model outperforms the other models in the prediction steps and can provide reliable driver’s head pose data for distraction warning and takeover rules.

Feasibility of automated in-vehicle technologies on volunteers’ driving performance

BACKGROUND: Automated in-vehicle technologies, specifically in-vehicle information systems (IVIS) and advanced driver assistance systems (ADAS), are increasingly common in today’s cars. Previous studies illustrate benefits of using IVIS and ADAS to improve safety, convenience, and comfort in healthy older drivers. However, research is sparse on the feasibility of such technologies for medically at-risk drivers, such as those with Parkinson’s disease (PD). OBJECTIVE: This study enrolled healthy volunteer drivers to examine the feasibility of the procedures and measures for evaluating the effects of IVIS and ADAS on their driving performance. METHODS: During this feasibility study researchers compared drives completed with and without support of IVIS and ADAS, as participants drove a 2019 Toyota Camry XLE. The test vehicle was equipped with IVIS, ADAS, cameras, a telematics system, and sensors. Participants drove the road course supervised by a Driver Rehabilitation Specialist (DRS). RESULTS: Overall study procedures and vehicle equipment were feasible and provided sufficient data collection for measuring the impact of IVIS and ADAS on driving performance. Data observation by the DRS combined with data captured from cameras and telematics, facilitated comparisons to increase data reliability and validity. CONCLUSIONS: Feasibility study findings informed a randomized clinical trial, examining the use of IVIS and ADAS technologies as an intervention to support drivers with Parkinson’s disease.

Evaluation of driver distraction from in-vehicle information systems: A simulator study of interaction modes and secondary tasks classes on eight production cars

Currently, various modalities for in-vehicle information systems have gained popularity. However, the secondary tasks of an in-vehicle information system (IVIS) may result in driver distraction while driving. This study aimed to evaluate the effects of classified secondary tasks performed in four modalities on driving performance and visual distraction. A driving simulator capable of connecting mass-produced cars to a virtual driving scenario was proposed. Ten secondary tasks were classified based on complexity and difficulty into three classes (basic, medium, and advanced). Thereafter, these tasks were tested using four modalities: knobs and buttons on the center stack, touch screen, steering wheel (SW) buttons, and voice control. For dependent measures, the standard deviation of speed, the standard deviation of lane position, fixation counts, and average fixation duration were collected. Consequently, significant primary effects of modality and task class were found in terms of each dependent measure. The analysis revealed that SW buttons and voice control were more suitable for basic tasks. Moreover, voice control was best for tasks in the medium class. In the advanced class, the voice control and touch screens outperformed the knobs and buttons. However, considering the efficiency and full interaction process, voice control is unsuitable for all advanced secondary tasks. Hence, cross-mode interaction was recommended for advanced class tasks with complicated input and feedback, owing to the trade-off between different interaction modes. The results of this study are significant for selecting the interaction modes and task design of IVIS for enhancing driving safety.