In-vehicle Information Systems Research Articles

Understanding the influence of in-vehicle information systems on range stress – Insights from an electric vehicle field experiment

Battery electric vehicle (BEV) drivers frequently report concerns of becoming stranded as a result of battery depletion, widely referred to as range stress, which constitutes a main obstacle to the adoption of these vehicles. One promising solution identified to mitigate range stress is to provide appropriate and accurate information through in-vehicle information systems (IVISs). Therefore, we aim to gain a broader understanding on the influence of specific IVISs on drivers’ range stress perception. To accomplish this, we performed a BEV field experiment in real traffic situations. By placing participants in a critical range situation, we could measure their psychometric range appraisal and psychophysiological feedback. The results of the psychometric evaluation revealed that individuals perceived the critical range situation as less challenging and threatening with the provided IVISs. Physiological indicators were found to be useful in complementing questionnaire-based evaluation methods within the field of range stress. Although the findings of the psychophysiological measurement revealed that IVISs-equipped participants exhibited increases in stress perception over time due to volatile range feedback, the average stress level was observed to be lower throughout the entire driving task.

Driver Distraction Using Visual-Based Sensors and Algorithms.

Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

Color Block Task

With the increasing use of in-vehicle information system (IVIS), driver distraction, especially visual distraction, has increased dramatically. Evaluating the impact of IVIS on traffic safety is important. Previous studies have used various standard secondary-task methods in place of real IVIS interactions to obtain quantitative and scalable results. However, traditional visual secondary tasks, such as the Surrogate Reference Task (SuRT) and the Arrow Task, are insufficient to simulate incrementally increased workload with fine details or contrast difference. This study designed a new form of visual secondary task (Color Block Task, CoBT) that could provide appropriate difficulty, incremental complexity, and contrast option for visual distraction assessment. The validity of this method was tested in a driving simulator by analyzing the reaction time and accuracy of the CoBTs, the reaction time and accuracy of a Tactile Detection Response Task (TDRT), and drivers’ vehicle control performance. Results showed that CoBT is a valid tool to mock drivers’ in-vehicle visual demand. CoBT and TDRT performance can effectively distinguish the difficulty levels of CoBT and explain drivers’ attention allocation pattern to some extent.

Intelligent Transport System: A Progressive Review

Objectives: Increase in traffic density in the world results in more and more congestion, air pollution and accidents. Hence Intelligent Transport System (ITS) has been emerged as a solution to various transport related issues. The aim of this research paper is to conduct systematic analysis on ITS. Methods/Statistical Analysis: ITS is defined as the set of applications which are advance and aim to apply intelligent information and communication technologies in order to provide services for transport and traffic management. ITS have combined various technologies such as Data collection, Communication, Data Mining, Machine Learning, Artificial Intelligence and Database Management. By combining these information technologies ITS have provided various applications such as Traffic control, Fault detection systems, In-vehicle information and navigation systems and driver assistance systems. Findings: We have considered the most relevant published work from 2008 onward relative to our objective from different popular digital libraries. We have summarized this work into issues in ITS and techniques used to solve them. Application/Improvements: It has been found that by combining various new technologies such as agent based computing, cloud computing, VANETS etc. ITS have become very efficient to solving transport related issues in smart cities.

Toward best practice in Human Machine Interface design for older drivers: A review of current design guidelines

Older adults are the fastest growing segment of the driving population. While there is a strong emphasis for older people to maintain their mobility, the safety of older drivers is a serious community concern. Frailty and declines in a range of age-related sensory, cognitive, and physical impairments can place older drivers at an increased risk of crash-related injuries and death. A number of studies have indicated that in-vehicle technologies such as Advanced Driver Assistance Systems (ADAS) and In-Vehicle Information Systems (IVIS) may provide assistance to older drivers. However, these technologies will only benefit older drivers if their design is congruent with the complex needs and diverse abilities of this driving cohort. The design of ADAS and IVIS is largely informed by automotive Human Machine Interface (HMI) guidelines. However, it is unclear to what extent the declining sensory, cognitive and physical capabilities of older drivers are addressed in the current guidelines. This paper provides a review of key current design guidelines for IVIS and ADAS with respect to the extent they address age-related changes in functional capacities. The review revealed that most of the HMI guidelines do not address design issues related to older driver impairments. In fact, in many guidelines driver age and sensory cognitive and physical impairments are not mentioned at all and where reference is made, it is typically very broad. Prescriptive advice on how to actually design a system so that it addresses the needs and limitations of older drivers is not provided. In order for older drivers to reap the full benefits that in-vehicle technology can afford, it is critical that further work establish how older driver limitations and capabilities can be supported by the system design process, including their inclusion into HMI design guidelines.

An Ecological Interface Design Approach for Developing Integrated and Advanced In-Vehicle Information System

The objective of this study is to propose a principled ecological interface design approach for in-vehicle information system and reduce driver's cognitive overload otherwise it might cause significant deterioration of driving performance and fatal consequences. Rasmussen's abstraction hierarchy was applied to analyze functional difference between the old and new cars. Information requirement analysis based on the Skill-Rule-Knowledge framework was conducted to classify the level of driver's cognitive load when interacting with modern car functions. It was found that driving assistance functions have been increased but their interfaces are not integrated nor standardized properly yet. Sometimes information is too much, too complex or not given at the right time. This study suggests to follow four principles of design for improving current in-vehicle information system: 1) Consolidation and Parsimony; 2) Abstraction and Integration; 3) Utilization of different display characteristics ; 4) Standardization through Customization . In this way of information design and distribution, people having different levels of driving experience would easily interact with new powerful functions of the car and stay in a safe, comfortable driving environment without losing connectivity.

Evaluating distraction of in-vehicle information systems while driving by predicting total eyes-off-road times with keystroke level modeling

Increasingly complex in-vehicle information systems (IVIS) have become available in the automotive vehicle interior. To ensure usability and safety of use while driving, the distraction potential of system-associated tasks is most often analyzed during the development process, either by employing empirical or analytical methods, with both families of methods offering certain advantages and disadvantages.The present paper introduces a method that combines the predictive precision of empirical methods with the economic advantages of analytical methods. Keystroke level modeling (KLM) was extended to a task-dependent modeling procedure for total eyes-off-road times (TEORT) resulting from system use while driving and demonstrated by conducting two subsequent simulator studies.The first study involved the operation of an IVIS by N = 18 participants. The results suggest a good model fit (R2Adj. = 0.67) for predicting the TEORT, relying on regressors from KLM and participant age. Using the parameter estimates from study 1, the predictive validity of the model was successfully tested during a second study with N = 14 participants using a version of the IVIS prototype with a revised design and task structure (rPred.-Obs. = 0.58). Possible applications and shortcomings of the approach are discussed.

An MCDM Approach to the Selection of Novel Technologies for Innovative In-Vehicle Information Systems

Driving a car is a complex skill that includes interacting with multiple systems inside the vehicle. Today's challenge in the automotive industry is to produce innovative In-Vehicle Information Systems (IVIS) that are pleasant to use and satisfy the costumers' needs while, simultaneously, maintaining the delicate balance of primary task vs. secondary tasks while driving. The authors report a MCDM approach for rank ordering a large heterogeneous set of human-machine interaction technologies; the final set consisted of hundred and one candidates. They measured candidate technologies on eight qualitative criteria that were defined by domain experts, using a group decision-making approach. The main objective was ordering alternatives by their decision score, not the selection of one or a small set of them. The authors' approach assisted decision makers in exploring the characteristics of the most promising technologies and they focused on analyzing the technologies in the top quartile, as measured by their MCDM model. Further, a clustering analysis of the top quartile revealed the presence of important criteria trade-offs.

에코 드라이빙 피드백 제공 빈도와 구체성이 연비와 작업부하에 미치는 효과

This study examined the relative effects of feedback frequency and specificity of Eco-IVIS(eco in-vehicle information system) on the fuel-efficiency and workload. Eighty participants randomly assigned into four experimental groups (high frequency/specific, high frequency/global, low frequency/specific, and low frequency/global feedback) and they drove 16.4Km motorway under the each feedback condition. The dependent variable were fuel efficiency and Drive Activity Load Index which measured participants' subjective ratings of driving workload. The results showed that high frequent feedback was more effective for increasing fuel-efficiency than low frequent feedback, however, there was no significant difference of fuel-efficiency between specific and global feedback. Although, overall DALI score was comparable among four experimental conditions, visual demand score was significant higher under the high frequent feedback condition than low frequent feedback.

Individual glance strategies and their effect on the NHTSA visual manual distraction test

The purpose of this paper was to investigate how individual differences in glance strategy could impact the glance performance test defined in the NHTSA visual manual distraction guidelines. Better understanding of the test procedure could help development of new technology for safe driving. A custom in-vehicle information system was developed and assessed in a driving simulator by eighteen participants. The interfaces were designed according to recommendations in the NHTSA guidelines and contained manual radio-tuning tasks, sound settings tasks and six letter spelling tasks. Two of the six tested interfaces fully complied with the test. In addition, clear individual differences in glance strategy were found among the participants. Four individual glance strategies were identified. Two of these, long glancers and frequent glancers, highly affected the outcome of the compliance test. Participants belonging to the long glancers and the frequent glancers categories were identified as statistical outliers in many test cases. For example, if the individual values of these participants were replaced with sample mean, the number of complying interfaces would increase to five out of six, which is more in line with expectations for these interfaces. The results of this study show that individual variations in glance strategy exist. Also, these individual variations seem to have a non- negligible influence on the result when performance-testing of in-vehicle interfaces is done according to the NTHSA guidelines.

Driver's adaptive glance behavior to in-vehicle information systems

The purpose of this study was to examine the adaptive behavior of drivers as they engage with in-vehicle devices over time and in varying driving situations. Behavioral adaptation has been shown to occur among drivers after prolonged use of in-vehicle devices, but few studies have examined drivers’ risk levels across different driving demands. A multi-day simulator study was conducted with 28 young drivers (under 30 years old) as they engaged in different text entry and reading tasks while driving in two different traffic conditions. Cluster analysis was used to categorize drivers based on their risk levels and random coefficient models were used to assess changes in drivers’ eye glance behavior. Glance duration significantly increased over time while drivers were performing text entry tasks but not for text reading tasks. High-risk drivers had longer maximum eyes-off-road when performing long text entry tasks compared to low-risk drivers, and this difference increased over time. The traffic condition also had a significant impact on drivers’ glance behavior. This study suggests that drivers may exhibit negative behavioral adaptation as they become more comfortable with using in-vehicle technologies over time. Results of this paper may provide guidance for the design of in-vehicle devices that adapt based on the context of the situation. It also demonstrates that random coefficient models can be used to obtain better estimations of driver behavior when there are large individual differences.

The effects of task demand on crew performance in simulated military land vehicles

New field vehicles, soon to be acquired by the Australian Army, will incorporate advanced in-vehicle information systems whose impact on operator workload and crewing requirements are yet to be examined. Three simulation studies were conducted as an exploration of the impact of existing and potential task demands on field vehicle crew performance. Army participants operated in driver/co-driver crews, performing in-vehicle tasks broadly analogous to those required in real-world field vehicles. Individual and crew performance was assessed under systematically manipulated task demands. Substantial declines in performance with time-on-task were found on simple repetitive vigilance and monitoring tasks, especially under the lowest task demand conditions. Increasing task demands or complexity generally helped mitigate those declines, but with a potential cost to overall performance. The results support effort regulation theory, with evidence of under-mobilization of engagement, effort and resources under monotonous, low demand conditions. Adaptive in-vehicle systems that monitor performance and allocate tasks to operators according to their functional state may prove beneficial. Study limitations and future studies in this area are discussed.

Looking and thinking when driving: The impact of gaze and cognitive load on steering

Driving around bends at high speeds is a task performed by many on a daily basis but the underlying mechanisms of steering control remain largely unknown. Previous research has shown that when steering, gaze direction can be a critical component of success. However, with increased use of in-vehicle information systems (IVIS), there is growing competition over the same resources that are needed to steer (gaze as well as associated attentional resources). Although it can be argued that locomotor steering is an automatic task that can be performed without recourse to conscious “cognitive” control, much simpler locomotor-related tasks, such as judging one’s heading, have been shown to be affected by concurrent attentional tasks (Wann, Swapp, & Rushton, 2000). Here we examined whether an attentional task placed at an offset fixation point influenced concurrent steering performance along a computer simulated road. The experiments either used gaze-fixation points that had similar properties to real-world road signs (i.e. moved relative to the vehicle) or were more akin to IVIS (i.e. fixed to the vehicle). Results showed that gaze fixation eccentric to future path caused systematic steering biases. The degree or type of cognitive load did not change the degree of steering bias, but there was some evidence of decreased lane variability when viewing the IVIS-type displays. No differences in steering performance were found between the different types of cognitive task. We conclude that where you look is critical for safe driving, and IVIS-type displays might make drivers more susceptible to cognitive interference.

Distraction effects of navigation and green-driving systems – results from field operational tests (FOTs) in the UK

Navigation systems and green-driving advisory systems are now a common feature in modern vehicles. The interaction of drivers with such systems when driving must be considered to minimise distraction whilst maintaining the benefits provided. This research investigates the glance behaviours of drivers, assessed from video data, when using a navigation device (study 1) and a green-driving advisory device (study 2). The main research question addressed was ‘Does the device cause visual distraction that might be detrimental to safety?’ The main focus was to establish the number of glances of 2 s or more to the systems and relate this to driver safety (as stipulated in new guidelines for use of in-vehicle systems proposed by the National Highway Traffic Safety Administration (NHTSA) in the US). Study 1 tested a Blom Ndrive G800 navigation device whilst study 2 tested the Foot-LITE device; a Smart’ driving system which incorporates Green Driving Support (GDS). In study 1, the subjects were specifically requested to drive a prescribed route of approximately 25 km and were directed by an experimenter who was present in the front passenger seat (baseline condition phase). After approximately 8 months, the same 10 subjects were requested to perform the same drive but using the navigation device to guide them around the route (experimental condition phase). In study 2, all subjects were asked to drive a prescribed route first without using the Foot-LITE device (baseline condition phase) and then some days later, with the Foot-LITE device (experimental condition phase). In study 1, the percentage of eyes off road time for drivers was much greater in the experimental (with device) condition compared to the baseline condition (14.3 % compared to 6.7 %) but whilst glances to the navigation device account for the majority of the increase, there are very few which exceed 2 s. Drivers in study 2 spent on average 4.3 % of their time looking at the system, at an average of 0.43 s per glance; no glances exceeded 2 s. In study 1, whilst glances to the personal navigation device account for the majority of the increase in eyes off road time (in accordance with intuitive expectations), when the durations of the glances are analysed, there were very few which are 2 s or more in duration. The average glance was 0.76 s which compared with average duration of glances to other target areas of 0.6 s, well below the proposed NHTSA guidelines. In study 2, the main outcome was that the total number of glances made was similar for each condition, at approximately 1100. Therefore glances to the in-vehicle Smart driving system during the experimental condition must have been reallocated from a different location. Around 75 % of the glances were between 0.2 and 0.6 s – also well below the NHTSA proposed guidelines. The results of the study have important design implications for future in-vehicle information systems.

Developing Visual Complexity Metrics for Automotive Human-Machine Interfaces

Objective: The purpose of this study is to develop visual complexity metrics based on theoretical bases.BR Background: With the development of IT technologies, drivers process a large amount of information caused by automotive human-machine interface (HMI), such as a cluster, a head-up display, and a center-fascia. In other words, these systems are becoming more complex and dynamic than traditional driving systems. Especially, these changes can lead to the increase of visual demands. Thus, a concept and tool is required to evaluate the complicated systems.BR Method: We reviewed prior studies in order to analyze the visual complexity. Based on complexity studies and human perceptual characteristics, the dimensions characterizing the visual complexity were determined and defined. BR Results: Based on a framework and complexity dimensions, a set of metrics for quantifying the visual complexity was developed.BR Conclusion: We suggest metrics in terms of perceived visual complexity that can evaluate the in-vehicle displays.BR Application: This study can provide the theoretical bases in order to evaluate complicated systems. In addition, it can quantitatively measure the visual complexity of In-vehicle information system and be helpful to design in terms of preventing risks, such as human error and distraction.

Adaptive Multimodal In-Vehicle Information System for Safe Driving

This paper proposes an adaptive multimodal in-vehicle information system for safe driving. The proposed system filters input information based on both the priority assigned to the information and the given driving situation, to effectively manage input information and intelligently provide information to the driver. It then interacts with the driver using an adaptive multimodal interface by considering both the driving workload and the driver's cognitive reaction to the information it provides. It is shown experimentally that the proposed system can promote driver safety and enhance a driver's understanding of the information it provides by filtering the input information. In addition, the system can reduce a driver's workload by selecting an appropriate modality and corresponding level with which to communicate. An analysis of subjective questionnaires regarding the proposed system reveals that more than 85% of the respondents are satisfied with it. The proposed system is expected to provide prioritized information through an easily understood modality.

Detecting Drivers’ Stress in Emergency Using Physiological Sensors

This paper presented methods for collecting and analyzing heart rate data during virtual-world driving task in emergency to distinguish and assess a driver’s stress state. The primer study had developed the driver stress training system loaded on driving simulator to display 14 typical emergency scenarios in different road environment. Electrocardiogram was recorded continuously by Physiological Sensors while drivers followed a random simulated driving on the driving simulator. Data from 30 drivers of at least 10 times per scenario were collected for analysis. The data were analyzed in two ways of HRV (Heart Rate Variability) analysis. Analysis I used time domain indexes of data during the driving in the whole training to distinguish the stress occur during the time frame of the scenario with an accuracy of over 98% across multiple drivers and driving days. Analysis II compared frequency indexes of data in stress state in emergency and in clam, with a metric of observable stressors created by the scenarios. The results showed that heart rate metrics is most closely correlated with driver stress level for most drivers studied. These findings confirmed that heart rate signals can provide a metric of driver stress in future cars capable of physiological monitoring. Such measurement could be used to help manage non-critical and critical in-vehicle information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers. Physiological sensors can be used in driving assistance system in future.

Methodological development of a specific tool for assessing acceptability of assistive systems of powered two‐wheeler‐riders

Research on the acceptability of assistive systems for improving the safety of powered two-wheelers (PTWs) is a pressing issue. The use of safety-enhancing assistive systems for motorised vehicles, including advanced driver assistance systems and in-vehicle information systems is widespread in many countries. Yet, there is only a limited number of equivalent intelligent transport systems (ITS) for PTWs, namely advanced rider assistance systems and on-bike information systems. This study describes the methodological development of a specific tool for assessing motorcyclists’ acceptability of ITS, as part of the motorcyclists’ profiling questionnaire (MOPROQ). There were three stages of development. First, a literature review was undertaken to assess the current state of the art regarding ITS for PTWs and to determine the most relevant facets of acceptability that should be measured. Second, a series of focus group interviews were conducted to explore riders’ attitudes towards ITS. Finally, the focus group results were used to develop a large-scale survey (MOPROQ), which was administered to an initial sample of over 6000 riders internationally. The designed tool can be used as a basis for the determination of rider acceptability of ITS systems in the future.

Relating the Detection Response Task to Critical Events – Consequences of high Cognitive Workload to Brake Reaction Times

Currently the use of new interaction modalities with in-vehicle information systems (IVIS), for example speech interaction, leads to a shift from visual-manual workload to more cognitive workload. The aim of this study was to better determine the link between increasing cognitive workload and corresponding reaction times that are relevant to the driving task, for example brake reactions to sudden events. In a driving simulator study with 35 middle-aged participants, subjects had to remember a series of digits ranging from 2 to 5 numbers, recall them in the reverse order and additionally react to suddenly appearing pedestrians. Cognitive workload was assessed by a head-mounted detection response task (DRT). It was hypothesized that with increasing difficulty of the cognitive task, hence increasing cognitive workload, reaction times to the DRT as well as brake reaction times to the pedestrian increase. DRT reaction time increased significantly with ascending number of digits. However, increasing cognitive workload did not affect brake reaction times in this setting. One reason might be the strong salience of pedestrians as a stimulus, leading to no increase in reaction time.

Effect of Multiple Resource Competition on Driving Performance Using an In-Vehicle Information System

The present study aims to clarify how four different combinations of input and output resources affect driving performance, because usage of information devices based on non-visual and non-manual interface during driving is configured as distracter recently. The primary task consisted of following a lead vehicle at a constant headway while that vehicle changed speed according to a predefined rhythm. The secondary task, which involved using a surrogate user interface, consisted of four different tasks to assess interference with the driver’s attentional resource during driving. To achieve the objective, we conducted a field experiment on a test track using 14 participants. The results suggest that the smallest effects on driving-relevant metrics were for the combination of auditory stimuli and verbal response, and that the greatest effects were for the combination of visual stimuli and manual response.