In-vehicle Human Machine Interface Research Articles

Adopting an HMI for overtaking assistance - Impact of distance display, advice, and guidance information on driver gaze and performance

Conveying supporting information by an in-vehicle human–machine interface (HMI) overtaking assistance system represents a promising approach to facilitate safe overtaking maneuvers. Although various researchers have attempted to examine the safety of systems, numerous HMI concepts are not validated and the impacts of systems on driver behavior following exposure to a connected environment remain unclear. Thus, the objective of this study is to assess the effectiveness of various HMI concepts and their effects on drivers' gaze behavior and driving performance in the connected environment. Three types of supporting information, namely distance, advice and guidance, were designed based on implicit and explicit concepts and displayed on the HMI. The guidance information provides more detailed maneuver instructions. A driving simulator experiment was conducted to imitate a connected environment and included an overtaking scenario on a two-lane highway under three distance gaps (short, middle and long) of oncoming vehicles and four HMI conditions including baseline (3*4). 39 participants (mean = 28.3 years, SD = 8.2 years) drove the simulator. Visual attention, driving maneuver, and subjective evaluation data were collected. The results indicated that the supporting information was valid in facilitating overtaking behavior compared without HMI, particularly under the middle distance gap scenario. Advice information was the most effective, required the least attention, exhibited the best performance, and was accepted by drivers. Detailed maneuver instructions were perceived as helpful, although no effect on acceptance was found. The findings of the study provide valuable insights into the development of user-friendly overtaking assistance systems.

Effects of collision warning characteristics on driving behaviors and safety in connected vehicle environments

With the emerging connected vehicle (CV) technologies, a novel in-vehicle omni-direction collision warning system (OCWS) is developed. For example, vehicles approaching from different directions can be detected, and advanced collision warnings caused by vehicles approaching from different directions can be provided. Effectiveness of OCWS in reducing crash and injury related to forward, rear-end and lateral collision is recognized. However, it is rare that the effects of collision warning characteristics including collision types and warning types on micro-level driver behaviors and safety performance is assessed. In this study, variations in drivers’ responses among different collision types and between visual only and visual plus auditory warnings are examined. In addition, moderating effects by driver characteristics including drivers’ demographics, years of driving experience, and annual driving distance are also considered. An in-vehicle human–machine interface (HMI) that can provide both visual and auditory warnings for forward, rear-end, and lateral collisions is installed on an instrumented vehicle. 51 drivers participate in the field tests. Performance indicators including relative speed change, time taken to accelerate/decelerate, and maximum lateral displacement are adopted to reflect drivers’ responses to collision warnings. Then, generalized estimation equation (GEE) approach is applied to examine the effects of drivers’ characteristics, collision type, warning type and their interaction on the driving performance. Results indicate that age, year of driving experience, collision type, and warning type can affect the driving performance. Findings should be indicative to the optimal design of in-vehicle HMI and thresholds for the activation of collision warnings that can increase the drivers’ awareness to collision warnings from different directions. Also, implementation of HMI can be customized with respect to individual driver characteristics.

A speed limit advisory system provided by in-vehicle HMI considering auditory perception characteristics for connected environment

In-vehicle HMI (Human Machine Interface) systems are well studied due to their great application prospect, but the recent development of connected technologies raises more possibilities that this technology provides more accurate speed limit warnings to ensure driver safety according to specific vehicle and road parameters. To capitalize on the potential of in-vehicle HMI systems to provide auditory and visual warnings to drivers and to improve adoption and utilization of this warning information. To this end, we designed a speed limit advisory system (SLAS) that recommended reduced speeds for hazardous sections of road, communicated to drivers by their in-vehicle HMI systems. Frontal distance models of single and gradient auditory warning were implanted into the systems to determine the warning timing. We then tested these SLASs in a high-fidelity simulator in order to evaluate their utility. 70 participants (mean age = 28.7 years; 36 male) drove through a simulated 15 km stretch of one-way two-lane rural road. We evaluated five different warning modes: single visual (SV), single audio visual (SAV), gradient visual (GV), gradient audio visual (GAV) or control (roadside sign). Participants using the SLAS slowed down significantly compared to the control group before entering the curve, with drivers receiving GAV warnings driving lowest speeds. Less experienced drivers exhibited higher compliance with GAMs (GV or GAV). The SLAS was generally considered useful though not by drivers with>10 years of driving experience. Drivers tended to prefer singular warnings to gradated warnings, which were rated as equally preferable to roadside signage. Eye tracking data indicated that none of the SLAS modes were overly distracting to the drivers. Overall, we find that the SLAS improves driver safety while producing no obvious distraction. The main difficulty lies in persuading experienced drivers to use the system. In a word, the results of this study provide validation for the concept of HMI warning system using connected technology in hazardous road areas and help determine the appropriate timing of in-vehicle warnings.

Cognitive Preference Performance of In-Vehicle Human–Machine Interface Icons under Female New Energy Vehicles

With the advent of the “her economy” era, the new energy automobile market has also ushered in the “her era”, and female consumers have gradually become the main force of domestic and foreign vehicle consumption, thus contributing to the sustainable and rapid development of many female new energy automobile market segments. In this context, this study explores the icon cognitive preferences of female drivers based on gender differences in icon cognition by taking the human–machine interface icons in new energy automobiles as a case study. Firstly, we conducted behavioral response experiments and facial electromyography experiments on 20 male and female participants to analyze their cognitive preferences for icons by combining the four dimensions of “semantic dimension, conceptual dimension, contextual dimension and pragmatic dimension”. The results showed that the four−dimensional graphic deconstruction format had a significant effect on the improvement of icon recognition performance. At the same time, we designed 10 formats of icons as experimental stimulus materials and combined them with subjective scales to jointly explore the reasons for the bias of different gender participants towards icons. The results show that there are significant gender differences in icon perception on a four−dimensional basis, with males more likely to be disturbed by icon constituent elements (semantic dimension), while females are more likely to be disturbed by icon metaphors (semantic dimension) and usage environment and interface context (contextual dimension). This study helps to explore the best balance between studying women’s driving experiences in new energy vehicles and the sustainable product life cycle, and then improve the accuracy of women drivers’ decision−making behavior in new energy vehicles to ensure driving safety.

Is the automated vehicle “aware” of the pedestrian? Examining driving behavior adaptation as a cue to inform the passenger of a potential hazard

In urban areas, drivers often have difficulty detecting pedestrians who want to cross the street but are obstructed by parked vehicles. Highly automated vehicles (HAVs, SAE Level 4) will have to solve this issue automatically and at least as safely as human drivers. We conducted two studies to investigate whether passengers’ trust in automation can be supported by adapting HAV driving behavior to demonstrate that the vehicle is “aware” of a potential hazard. We examined the passengers’ perception of risk, their trust in the automated driving system, and their preferences for HAV behavioral adaptations as a response to potential hazard in a driving situation in two experimental studies, 1) a driving simulator study and 2) a human&vehicle-in-the-loop study. We varied the lateral offset from the road center and deceleration (driving simulator only) when passing a parked vehicle. In the driving simulator study, we varied the HAV’s speed and the presence of a pedestrian in the parking bay and an oncoming vehicle. In the human&vehicle-in-the-loop study, the HAV passing the obscured pedestrian at 50 km/h. Results support the idea that it is beneficial for human passengers to be informed about the HAVs “awareness” of a potentially hazardous situation. However, introducing a lateral offset had no positive effect on passengers’ perceptions. Deceleration enhanced passengers’ trust but was only examined in the driving simulator study. Future research should compare the effects of behavioral adaptations to other in-vehicle human–machine interface approaches providing information about the “awareness” about potential hazards visually.

Chinese handwriting while driving: Effects of handwritten box size on in-vehicle information systems usability and driver distraction

Objective Handwritten box size (HBS) is an essential Chinese handwriting interface element when interacting with touchscreen-based in-vehicle information systems (IVISs) since it is compactly bound up with driver distraction besides usability issues. Miscellaneous HBSs are commercially available on IVISs, yet the details of how the drivers interact with them in an in-vehicle display situation remain sparsely examined. Therefore, this study set out to investigate the effects of HBS on IVISs usability (task completion time, number of errors, number of protruding strokes, and NASA-TLX, a subjective workload assessment tool) and driver distraction (mean speed, lane position variation, total glance time, number of glances, mean glance time, and number of glances exceeding 1.6 s). Ultimately, the appropriate HBS on IVISs is determined. Methods A simulated driving experiment involving thirty drivers was launched. The primary task was lane-keeping with speed ranging from 40 to 60 km/h, and the secondary task was entering a 5-character text by Chinese handwriting input under five different HBS conditions: 25 × 25 mm, 30 × 30 mm, 35 × 35 mm, 40 × 40 mm, and 45 × 45 mm. A battery of one-way repeated measure analyses of variance (r-ANOVA) was used to examine which HBS can maximize IVISs usability and minimize driver distraction with the smallest HBS. Results Generally, the issues of IVISs usability and driver distraction improved progressively as the HBS increased to a specific size (40 × 40 mm in this study), at which they got to the asymptotes. Specifically, HBS below 40 × 40 mm was associated with longer text input time, more errors and protruding strokes, extended eyes-off-road time, excessive off-road glances, and deteriorative lateral driving performance. Nevertheless, there were no significant differences in mean glance time and longitudinal driving performance. No improvement measures were observed for HBS above 40 × 40 mm, except for a higher perceived workload. Conclusions Overall, the appropriate HBS of in-vehicle Chinese handwriting was found to be 40 × 40 mm. Considering that the in-vehicle human-machine interface (HMI) has limited display space and increasing visual complexity, these findings may help develop evidence-based design guidelines for driver-friendly IVISs and prevent distracted-related traffic injuries.

How to Display Vehicle Information to Users of Automated Vehicles When Conducting Non-Driving-Related Activities

Automated vehicles (AVs) are expected to enable users to engage in non-driving-related activities (NDRAs). However, users do not easily trust an automated vehicle which poses new challenges for automotive human-machine interfaces (HMIs). Over-presenting vehicle information can distract users from NDRAs, and under-presentation can impact trust and user experience (UX) negatively. To investigate how to best present vehicle information to foster users' trust and UX while performing NDRAs, we designed two in-vehicle HMI concepts: 1) A colored and animated light bar display around the windshield and 2) a windshield display interface presenting pictograms and numbers. Results from a simulator study ($N=18$) indicate that both concepts contribute to a high trust level and UX while not affecting the NDRA performance compared to the baseline of not showing vehicle information. In addition, the light bar provides better UX than the windshield display and is also preferred by users. With our findings, we contribute to the effective design of presenting vehicle information in automated vehicles.

Transition patterns of driving style from a traditional driving environment to a connected vehicle environment: A case of an extra-long tunnel road

To provide a better understanding of individual driver’s driving style classification in a traditional and a CV environment, spatiotemporal characteristics of vehicle trajectories on a road tunnel were extracted through a driving simulator-based experiment. Speed, acceleration, and rate of acceleration changes are selected as clustering indexes. The dynamic time warping and k-means clustering were adopted to classify participants into different risk level groups. To assess the driver behavior benefits in a CV environment, an indicator BI (behavior indicator, BI) was defined based on the standard deviation of speed, the standard deviation of acceleration, and the standard deviation of the rate of acceleration change. Then, the index BI of each driver was calculated. Furthermore, this paper explored driving style classification, not in terms of traditional driving environment, but rather the transition patterns from a traditional driving environment to a CV environment. The results revealed that inside a long tunnel, 80 % of drivers benefited from a CV environment. Moreover, drivers might need training before using a CV system, especially female drivers who have low driving mileage. In addition, the results showed that the driving style of 69 % of the drivers’ transferred from a high risk-level to a low risk-level when driving in a CV environment. The study results can be expected to improve driving training education programs and also to provide a valuable reference for developing individual in-vehicle human-machine interface projects and other proactive safety countermeasures.

A Literature Review on Additional Semantic Information Conveyed from Driving Automation Systems to Drivers through Advanced In-Vehicle HMI Just Before, During, and Right After Takeover Request

In-vehicle human-machine interface (HMI) plays a significant role in accomplishing effective interactions between driving automation systems and drivers, especially during the transition of control. For this reason, different in-vehicle HMIs have been designed to convey additional semantic information from the driving automation systems to the drivers to realize safer, smoother, and better control transitions. This review summarizes and analyses 86 previously published studies that researched the effects of additional semantic information delivered through in-vehicle HMIs just before, during and right after takeover request (TOR). The additional semantic information mentioned in this review refer to the information beyond simple alerts to not only gain drivers’ attention but also additionally communicate contextual content and explanation to the drivers regarding its own purpose. In this review, the additional semantic information are categorized according to their purposes and effects into three aspects: mode awareness enhancement, situation awareness enhancement, and takeover maneuver assistance. The specificities and the corresponding concerns when applying additional semantic information to in-vehicle HMIs have been detailed analyzed throughout the entire article. Further suggestions are proposed for what should be carefully considered when adding additional information for better takeover. Prospects into future in-vehicle HMI possibilities are also raised that could be applied in both academic research and industry.

In-Vehicle Human Machine Interface: Investigating the Effects of Tactile Displays on Information Presentation in Automated Vehicles

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</b> Semi-autonomous vehicles still require human drivers to take over when the automated systems can no longer perform the driving task. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</b> The goal of this study was to design and test the effects of six meaningful tactile signal types, representing six driving scenarios (i.e., navigation, speed, surrounding vehicles, over the speed limit, headway reductions, and pedestrian status) respectively, and two pattern durations (lower and higher urgencies), on drivers’ perception and performance during automated driving. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</b> Sixteen volunteers participated in an experiment utilizing a medium-fidelity driving simulator presenting vibrotactile signals via 20 tactors embedded in the seat back, pan, and belt. Participants completed four separate driving sessions with 30 tactile signals presented randomly throughout each drive. Reaction times (RT), interpretation accuracy, and subjective ratings were measured. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</b> Results illustrated shorter RTs and higher intuitive ratings for higher urgency patterns than lower urgency patterns. Pedestrian status and headway reduction signals were associated with shorter RTs and increased confidence ratings, compared to other tactile signal types. Lastly, among six tactile signals, surrounding vehicle and navigation signal types had the highest interpretation accuracy. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</b> These results will be used as preliminary data for future studies that aim to investigate the effects of meaningful tactile displays on automated vehicle takeover performance in complex situations (e.g., urban areas) where actual takeovers are required. The findings of this study will inform the design of next-generation in-vehicle human-machine interfaces.

The power and sensitivity of four core driver workload measures for benchmarking the distraction potential of new driver vehicle interfaces

This study evaluated the power and sensitivity of several core driver workload measures in order to better understand their use as a component of future driver distraction potential evaluation procedures of the in-vehicle human machine interface (HMI). Driving is a task that requires visual, manual and cognitive resources to perform. Secondary tasks, such as mobile phone use and interaction with in-built navigation, which load onto any of these three processing resources increase driver workload and can lead to impaired driving. Because workload and distraction potential are interrelated, a comprehensive method to assess driver workload that produces valid and predictive results is needed to advance the science of distraction potential evaluation. It is also needed to incorporate into New Car Assessment Program (NCAP) testing regimes. Workload measures of cognitive (DRT [Detection Response Task] Reaction Time), visual (DRT Miss Rate), subjective (NASA-TLX [driver workload questionnaire]), and temporal demand (Task Interaction Time) were collected as participants drove one of 40 vehicles while completing a variety of secondary tasks with varying interaction requirements. Of the evaluated measures, variance and power analyses demonstrated that Task Interaction Time is the most sensitive in detecting differences in driver workload between different in-vehicle HMIs, followed by DRT Miss Rate, NASA-TLX and finally DRT Reaction Time. There were relatively weak correlations between each of the four measures. These results suggest that Task Interaction Time, coupled with a reliable visual demand metric such as DRT Miss Rate, eye glance coding, or visual occlusion, more efficiently detect differences in driver workload between different HMIs compared to DRT Reaction Time and the NASA-TLX questionnaire. These results can be used to improve the understanding of the utility of each of these core driver workload measures in assessing driver distraction potential.

An Accessible Autonomous Vehicle Ridesharing Ecosystem

Autonomous ridesharing vehicles provide a potential alternative and affordable mode of transportation for older adults and users with disabilities or special needs, but relatively few studies have investigated the accessibility of autonomous ridesharing applications and vehicles for them. Therefore, a participatory design approach was used to investigate the needs and challenges of this population. This investigation involved interviews, review of past focus group transcripts, literature reviews, and surveys. The findings resulted in a list of user needs to design and build prototypes of accessible technologies as part of an ecosystem for booking and riding in autonomous ridesharing vehicles. The ecosystem of technologies consisted of prototypes of a mobile application, a web application, an in-vehicle human-machine interface, an external human-machine interface, and the vehicle interior design.

Improving Passenger Experience and Trust in Automated Vehicles Through User-Adaptive HMIs: “The More the Better” Does Not Apply to Everyone

Automated vehicles promise transformational benefits for future mobility systems, but only if they will be used regularly. However, due to the associated loss of control and fundamental change of in-vehicle user experience (shifting from active driver to passive passenger experience), many humans have reservations toward driving automation, which question their sufficient usage and market penetration. These reservations vary based on individual characteristics such as initial attitudes. User-adaptive in-vehicle Human-Machine Interfaces (HMIs) meeting varying user requirements may represent an important component of higher-level automated vehicles providing a pleasant and trustworthy passenger experience despite these barriers. In a driving simulator study, we evaluated the effects of two HMI versions (with permanent vs. context-adaptive information availability) on the passenger experience (perceived safety, understanding of driving behavior, driving comfort, driving enjoyment) and trust in automated vehicles of 50 first-time users with varying initial trust (lower vs. higher trust group). Additionally, we compared the user experience of both HMIs. Presenting driving-related information via HMI during driving improved all assessed aspects of passenger experience and trust. The higher trust group experienced automated driving as safest, most understandable and most comfortable with the context-adaptive HMI, while the lower trust group tended to experience the highest safety, understanding and comfort with the permanent HMI. Both HMIs received positive user experience ratings. The context-adaptive HMI received generally more positive ratings, even though this preference was more pronounced for the higher trust group. The results demonstrate the potential of increasing the system transparency of higher-level automated vehicles through HMIs to enhance users’ passenger experience and trust. They also consolidate previous findings on varying user requirements based on individual characteristics. User group-specific HMI effects on passenger experience support the relevance of user-adaptive HMI concepts addressing varying needs of different users by customizing HMI features, such as information availability. Consequently, providing full information permanently cannot be recommended as a universal standard for HMIs in automated vehicles. These insights represent next steps toward a pleasant and trustworthy passenger experience in higher-level automated vehicles for everyone, and support their market acceptance and thus the realization of their expected benefits for future mobility and society.

Urgent and non-urgent takeovers during conditional automated driving on public roads: The impact of different training programmes

Supplying training to drivers that teaches them about automated driving and requests to intervene may help them to build and maintain a mental representation of how automation works and thereby improve takeover performance. We aimed to investigate the effect of different types of training programmes about the functioning of automated driving on drivers’ takeover performance during real driving. Fifty-two participants were split into three groups for training sessions: paper (short notice), video (3-minute tutorial) and practice (short drive). After the training, participants experienced automated driving and both urgent and non-urgent requests to intervene in a Wizard-of-Oz vehicle on public roads. Participants’ takeover time, visual behaviour, mental workload, and flow levels during the requests to intervene were assessed. Our results indicated that in urgent circumstances, participants’ takeover response times were faster in the practice training condition compared to the other training conditions. Nevertheless, the practice training session did not present any other positive effect on drivers’ visual behaviour. This could indicate that prior training, particularly when reinforcing drivers' motor skills, improved their takeover response time at the latest motor stages rather than in the early sensory states. In addition, the analysis of in-vehicle videos revealed that participants’ attention was captured in the first place by the in-vehicle human-machine interface during the urgent request to intervene. This highlights the importance for designers to display information on the HMI in an appropriate way to optimise drivers’ situation awareness in critical situations.

Exploring relationships between design features and system usability of intelligent car human–machine interface

In-vehicle human–machine interface (HMI) mainly refers to the T-shaped panel system with instruments, centre console, gear lever and other components installed. For intelligent vehicles, the high level of intelligent interconnection may to some extent make drivers lack situational safety awareness and reduce the usability of the system. Thus, this study attempted to establish a relationship between design features and system usability of the in-vehicle panels. From the perspective of visual ergonomics, the panels were deconstructed into design features to determine 36 samples to be studied. After dividing each sample into four areas of interest (AOI), eye movement and subjective preference data were collected to quantify the user experience. Artificial neural network (ANN) and support vector machine (SVM) were used in the study. Nevertheless, conventional learning algorithms often underwent deficiencies in accuracy and robustness in the detection of multifarious kinds of panels. Therefore, the parameters of the two models were tuned to deal with the noise common in user experience data. The determinant coefficients, mean-square errors and mean relative errors of the two models showed that the SVM model had a higher accuracy, smaller error and was more stable in the learning of user experience of HMI design features, which could provide a method for the layout design and evaluation of T-shaped instrument panel.

Impact of connected vehicle environment on driving performance: A case of an extra‐long tunnel scenario

In view of the benefits and a promising prospect of the emerging connected vehicles (CV), more and more vehicles will be equipped with the in-vehicle human-machine interface (HMI). This study aims to assess the comprehensive impacts of HMIs on individual driving performance, traffic safety, and eco-driving behaviour based on a driving simulator. In order to explore how a driver responds to warnings when approaching a tunnel, this study divided the experimental road into five zones according to warning positions. A comparative analysis was conducted to examine the changes in driving comfort, speeding behaviour, the standard deviation of speed, time headway (THW), fuel consumption, and emissions. Results showed that the HMI could be beneficial to speed adjustment before entering a tunnel. Meanwhile, HMI may affect the THW of vehicles. Drivers were usually more cautious when the HMI was on. However, no significant effects of the HMI for driving comfort and eco-driving behaviour can be observed. It is expected that more efficient technology and different warning strategies can be developed to enhance eco-safe driving for different zones because they have different effects. The findings of this study are also helpful for active safety management and for evaluating the effectiveness of CV systems.

Exploring drivers’ mental workload and visual demand while using an in-vehicle HMI for eco-safe driving

Eco-safe driving is a promising approach to improve road safety while reducing transport emissions. The application of an eco-safe driving system is feasible with the support of vehicle-to-vehicle/infrastructure technologies. To guarantee system usability and safety appropriateness, a key precondition is to ensure that driver mental workload and visual demands required for using the system are reasonable. This study explored how drivers’ mental workload and visual demands were affected when driving with an eco-safe driving HMI (human-machine-interface). Four in-vehicle eco-safe HMI information conditions were evaluated, including baseline, advice only, feedback only, and advice & feedback. Two traffic scenarios (stop-sign intersection with traffic vs. stop-sign intersection without traffic) were simulated using an advanced driving simulator. Behavioural variables (e.g. brake force, acceleration), visual variables (e.g. blink metrics, pupil size) and subjective workload scores were collected from 36 licensed Australian drivers. The experiment results showed that the HMI prompted drivers to apply a smooth and stable brake force when they approached the intersection and a smooth acceleration when they left the intersection. Drivers’ mental workload indicated by visual measurements were consistent with their subjective reported workload levels. Drivers had a higher mental workload when they received and processed additional eco-safe information in the advice & feedback condition. An increase in mental workload induced by the in-vehicle cognitive task initiated more blink activities while the increase in visual demand caused by a complex road situation led to blink inhibition. The study shows the HMI could significantly promote eco-safe driving behaivours without causing excessive mental and visual workload of drivers.

Spatiotemporal Characteristics of Vehicle Trajectories in a Connected Vehicle Environment—A Case of an Extra-Long Tunnel Scenario

To provide a better understanding of spatiotemporal characteristics of vehicle trajectories in connected vehicle environment, a driving simulation study was designed and conducted with an extra-long tunnel scenario. 35 drivers were recruited to participate in the driving experiment. To evaluate the spatiotemporal characteristics of vehicles with and without a warning system, objective measures were analyzed, including a spatiotemporal diagram of the curvature of obtained data and speed adjustment behaviors. This article also evaluated the impacts of connected vehicles on the traffic capacity based on the converging pattern mining method. The results indicated that the in-vehicle human-machine interface (HMI) improved driving behavior and traffic capacity. Notably, the in-vehicle HMI helped drivers better prepare for speed adjustments when approaching the tunnel and when the vehicle in front of the study vehicle made a sudden operational change. Moreover, the system contributed to a more stable operation speed, especially near the tunnel entrance, than that without the system. The findings suggest that connected vehicle environments enable drivers to change from traditional visual stimuli response behaviors to proactive response behaviors based on psychological expectations. Besides, based on the best-converging patterns from the spatiotemporal trajectories of 35 drivers, the results revealed that the traffic capacity could be improved by 22.19% under the experimental traffic flow conditions. Moreover, the differences in the benefits of the in-vehicle HMI among individuals were found to be statistically significant.

Challenges and Opportunities in Developing Next Generation In-Vehicle HMI Systems

The next generation of automotive human machine interface (HMI) systems is expected to be heavily dependent upon artificial intelligence; from autonomous driving to speech assistance, from gesture &amp; touch-enabled interfaces to web &amp; mobile integration. Smooth, safe, and user-friendly interaction between the driver and the vehicle is a key to winning market share. This panel aims to discuss challenges and opportunities for the next generation of automotive HMI from the perspective of human factors and user behavior. Panelists from industry and academia will offer their unique perspectives on the concerns and opportunities in developing future in-vehicle HMIs.

A simulator study of the effect of incentive on adoption and effectiveness of an in-vehicle human machine interface

IntroductionRapid developments in transportation technologies, such as in-vehicle human-machine interfaces (HMI), have the potential to improve driving behaviour. However, the use of such approaches is typically voluntary and there are numerous barriers to their widespread implementation. The aim of the current paper is to evaluate the impact of monetary incentive combined with competition with other drivers on adoption and effectiveness of an eco-safe in-vehicle HMI. Moreover, this research assess intentions to use and willingness to purchase the in-vehicle HMI, both of which play crucial roles in sustained voluntary uptake of in-vehicle HMIs. MethodForty drivers participated in a driving simulator experiment and questionnaires. Three variations of an eco-safe driving in-vehicle HMI were evaluated (advice only, feedback only, combined advice and feedback), followed by an incentive-based condition. ResultsThe findings revealed the 4.7% reduction in fuel consumption with an addition of incentive and competition with other drivers associated with the use of in-vehicle HMI on eco-safe driving behaviour. Moreover, there was some evidence to suggest that a range of extrinsic and intrinsic incentives may be beneficial for increasing intentions to use such a system. ConclusionsWe conclude that the addition of incentives may be more effective in encouraging greater intentions to use the in-vehicle HMI, compared to improving eco-safe driving behaviour associated with system use. Practical applicationsThis research provides valuable knowledge towards enhancing the current understanding of the nature and features of eco-safe in vehicle HMIs. Such information provides a foundation for the design and development of novel in-vehicle systems, incorporating the influence of competition with other drivers and incentives to enhance the motivation to use in-vehicle systems and consequently, improve drivers’ fuel efficiency and safe driving behaviour.