Use Of In-vehicle Information Systems Research Articles

Feasibility of automated in-vehicle technologies on volunteers' driving performance.

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). 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. 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). 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. 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.

Drivers Use of In-Vehicle Information Systems and Perceptions of Their Effects on Driving

In recent years the number of cars on the road with manufacturer installed in-vehicle information systems (IVIS) has increased dramatically, and with smartphones connecting directly to these systems drivers can access a wide range of applications on the move. Some IVIS features are designed to improve safety (e.g., speed and collision warnings) but access via an IVIS to all apps on a smartphone has the potential to distract drivers and increase crash risk. We undertook this study to find out what IVIS features and apps drivers use and to ask about their effects on driving. An online survey completed by 1017 drivers (50% female, 16- 85 years) revealed the most common activity drivers engaged in was listening to music, followed by listening to directions. Fewer than half of the drivers reported conversing on a hands-free phone and 25% reported texting/sending emails or using social media. Frequency of engagement in secondary tasks decreased with age. Using hand-held mobiles was rated as having the greatest negative impact on driving, with drivers attempting to mitigate the risk by carrying out the activity in slow moving traffic or at traffic lights. Females rated the risk of most of the secondary tasks as greater than males. Almost half of the sample had access to speed warnings systems but only a small proportion had experience of lane departure, dangerous curve or intersection warning systems. Of those with access to driver safety systems, 50-70% never used them despite rating the system as useful and as having safety benefits. As the number of vehicles with manufacturer installed IVIS continues to increase on the road, drivers’ engagement with them while driving is also likely to increase. To address this, there is a need for a multi-agency approach to educate drivers on the safe use of these systems and apps in their vehicles. Incentives for drivers to use the safety-related features may also be worth considering.

How do perceptions of risk and other psychological factors influence the use of in-vehicle information systems (IVIS)?

In-vehicle information systems (IVIS) have increased in popularity and availability in the past five years and are expected to transform the way individuals drive. Therefore, it is important to investigate the potential risks associated with IVIS use, particularly with regards to distracted driving. Thus, the aim of this study was to investigate drivers’ engagement with IVIS and related risky driving behaviours. Specifically, the current study explored drivers’ current knowledge and use of IVIS and perceptions of risk, and used the Theory of Planned Behaviour to investigate the psychological factors that influence drivers’ decision-making in relation to its use. A semi-structured interview was conducted with 32 Australian drivers aged 19–70 years who drove vehicles with an IVIS. The results showed that drivers mainly used their IVIS for entertainment (i.e., music) and navigation purposes (i.e. GPS), and lacked comprehensive knowledge of other capabilities. Additionally, drivers do not believe an IVIS is an essential system, and further stated that it would be risky if drivers relied on the IVIS for engagement in driving tasks (e.g., navigation) or non-driving tasks (e.g., mobile phone). Some of the factors that influenced drivers’ decisions to use IVIS were the safety, entertainment, and convenience it offered and the influence of family and friends. Finally, there is evidence that mobile phones are often preferred over IVIS while driving, based on perceptions that IVIS have lower ease of use and usability.

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.

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.

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.

Effect of Driver's Cognitive Distraction on Driver's Physiological State and Driving Performance

Objective: The aim of this study is to investigate effect of driver's cognitive distraction on driver's physiological state and driving performance, and then to determine parameters appropriate for detecting the cognitive distraction. Background: Driver distraction is a major cause of traffic accidents and poses a serious threat to traffic safety due to ever increasing use of in-vehicle information systems and mobile phones during driving. Cognitive distraction, among four different types of distractions, prevents a driver from processing traffic information correctly and adapting to change in surround vehicle behavior in time. However, the cognitive distraction is more difficult to detect because it normally does not involve significant change in driver behavior. Method: A full-scale driving simulator was used to create virtual driving environment and situations. Participants in the experiment drove the driving simulator in three different conditions: attentive driving with no secondary task, driving and conducting secondary task of adding numbers, and driving and conducting secondary task of conversing with an experimenter. Parameters related with driver's physiological state and driving performance were measured and analyzed for their change. Results: The experiment results show that driver's cognitive distraction, induced by secondary task of addition and conversation during driving, increased driver's cognitive workload, and indeed brought change in driver's physiological state and degraded driving performance. Conclusion: The galvanic skin response, pupil size, steering reversal rate, and driver reaction time are shown to be statistically significant for detecting cognitive distraction. The appropriate combination of these parameters will be used to detect the cognitive distraction and estimate risk of traffic accidents in real-time for a driver distraction warning system.

A reference level for assessing the acceptable visual demand of in-vehicle information systems

With the increasing use of in-vehicle information systems (IVIS) by drivers whilst the vehicle is in motion, the risk of distraction-related crashes is expected to increase. Distraction in this case arises from engagement of the driver with the visual demand of an IVIS display, but measurement of such visual demand, and design decisions about how much visual demand is acceptable in this context, is problematic. Using the visual ‘occlusion’ technique, this paper uses data from visual demand metrics (from 4 reference in-vehicle tasks with 60 participants) and makes comparisons with several other approaches including expert usability analysis, other reference levels, social acceptability survey data, and a comparison with alcohol impairment. Based on these considerations an approach is taken to represent the distribution of occlusion measurements and a demand reference level (DRL) is proposed to be used as a criterion for design of IVIS displays. The DRL comprises a metric derived from occlusion measurements and an absolute value.

Driver distraction based lane-keeping assistance

Abstract Driver assistance systems could be much more effective, if they were adaptive to the driver’s state. Following the idea of a lane-keeping assistance system, which is adaptive to the use of in-vehicle information systems (IVIS), two field experiments were conducted. The first experiment concerned the influence of typical IVIS tasks on lane-keeping. Most IVIS tasks increased the lateral deviations, but in the majority of cases the drivers were still able to stay in their lane. The second experiment concerned two questions: (1) how much lateral support would be needed and (2) how drivers accept this kind of support. The results show that all lateral support algorithms increased the lane-keeping performance with the algorithms providing a higher amount of assistance proving the most useful. All assistance systems were rated as helpful and were considered to increase driver safety, both by the drivers who did not have problems in lane-keeping without assistance. In light of these results an adaptation of a lateral support system to the IVIS-use seems to be useful and worthwhile.

Skill Acquisition While Operating In-Vehicle Information Systems: Interface Design Determines the Level of Safety-Relevant Distractions

This study tested whether the ease of learning to use human-machine interfaces of in-vehicle information systems (IVIS) can be assessed at standstill. Assessing the attentional demand of IVIS should include an evaluation of ease of learning, because the use of IVIS at low skill levels may create safety-relevant distractions. Skill acquisition in operating IVIS was quantified by fitting the power law of practice to training data sets collected in a driving study and at standstill. Participants practiced manual destination entry with two route guidance systems differing in cognitive demand. In Experiment 1, a sample of middle-aged participants was trained while steering routes of varying driving demands. In Experiment 2, another sample of middle-aged participants was trained at standstill. In Experiment 1, display glance times were less affected by driving demands than by total task times and decreased at slightly higher speed-up rates (0.02 higher on average) than task times collected at standstill in Experiment 2. The system interface that minimized cognitive demand was operated more quickly and was easier to learn. Its system delays increased static task times, which still predicted 58% of variance in display glance times compared with even 76% for the second system. The ease of learning to use an IVIS interface and the decrease in attentional demand with training can be assessed at standstill. Fitting the power law of practice to static task times yields parameters that predict display glance times while driving, which makes it possible to compare interfaces with regard to ease of learning.

Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines

As use of in-vehicle information systems (IVISs) such as cell phones, navigation systems, and satellite radios has increased, driver distraction has become an important and growing safety concern. A promising way to overcome this problem is to detect driver distraction and adapt in-vehicle systems accordingly to mitigate such distractions. To realize this strategy, this paper applied support vector machines (SVMs), which is a data mining method, to develop a real-time approach for detecting cognitive distraction using drivers' eye movements and driving performance data. Data were collected in a simulator experiment in which ten participants interacted with an IVIS while driving. The data were used to train and test both SVM and logistic regression models, and three different model characteristics were investigated: how distraction was defined, which data were input to the model, and how the input data were summarized. The results show that the SVM models were able to detect driver distraction with an average accuracy of 81.1%, outperforming more traditional logistic regression models. The best performing model (96.1% accuracy) resulted when distraction was defined using experimental conditions (i.e., IVIS drive or baseline drive), the input data were comprised of eye movement and driving measures, and these data were summarized over a 40-s window with 95% overlap of windows. These results demonstrate that eye movements and simple measures of driving performance can be used to detect driver distraction in real time. Potential applications of this paper include the design of adaptive in-vehicle systems and the evaluation of driver distraction

Nonintrusive Detection of Driver Cognitive Distraction in Real Time Using Bayesian Networks

Driver distraction has become an important and growing safety concern as the use of in-vehicle information systems (IVISs), such as cell phones and navigation systems, continues to increase. One approach to allowing people to benefit from IVISs without compromising safety is to create adaptive IVISs that adjust their functions according to driver and roadway state. A critical element of adaptive IVISs involves monitoring driver distraction in real time; with such a monitoring function it is possible to mitigate that distraction. This study applied Bayesian networks (BNs), a data mining method, to develop a real-time approach to detecting cognitive distraction using drivers’ eye movements and driving performance. Data were collected in a simulator experiment involving 10 participants who interacted with an IVIS system while driving. BN models were trained and tested to investigate the influence of three model characteristics on distraction detection: time history of driver behavior, inclusion of hidden nodes in the model structure, and how data are summarized and the length of training sequences. Results showed that BNs could identify driver distraction reliably with an average accuracy of 80.1%. Dynamic BNs (DBNs) that consider time dependencies of driver behavior produced more sensitive models than static BNs (SBNs). Longer training sequences improved DBN model performance. Blink frequency and fixation measures were particularly indicative of distraction. These results demonstrate that BNs, especially DBNs, can detect driver cognitive distraction by extracting information from complex behavioral data. Potential applications include the design of adaptive in-vehicle systems and the evaluation of driver distraction.

Peripheral detection as a workload measure in driving: Effects of traffic complexity and route guidance system use in a driving study

Recent studies have explored a promising method to measure driver workload—the Peripheral Detection Task (PDT). The PDT has been suggested as a standard method to assess safety-relevant workload from the use of in-vehicle information systems (IVIS) while driving. This paper reports the German part of a Swedish-German cooperative study in which the PDT was investigated focusing on its specific sensitivity compared with alternative workload measures. Forty-nine professional drivers performed the PDT while following route guidance system instructions on an inner-city route. The route consisted of both highly demanding and less demanding sections. Two route guidance systems that differed mainly in display size and display organization were compared. Subjective workload ratings (NASA-TLX) as well as physiological measures (heart rate and heart rate variability) were collected as reference data. The PDT showed sensitivity to route demands. Despite their differing displays, both route guidance systems affected PDT performance similarly in intervals of several minutes. However, the PDT proved sensitive to peaks in workload from IVIS use and from the driving task. Peaks in workload were studied by video analyses of four selected subsections on the route. Subjective workload ratings reflected overall route demands and also did not indicate differing effects of the two displays. The physiological measures were less sensitive to workload and indicated emotional strain as well. An assessment of the PDT as a method for the measurement of safety-related workload is given.