Driver trust in and training for advanced driver assistance systems in Real-World driving

Two-hundred and twenty-three participants completed an online survey regarding their experiences with advanced driver assistance systems (ADAS) on their personal vehicles, with focus on 1) drivers’ trust in 13 ADAS technologies, and 2) perceived effectiveness of currently used methods of training. Eighteen drivers participated in focus groups designed to probe more deeply into survey responses. Results of the survey showed that participant ratings of trust increased significantly with longer vehicle ownership, but participants who experienced unexpected ADAS technology behavior rated their trust over time significantly lower on ADAS technologies with the exception of rear collision avoidance. The majority (75.8%) of participants reported receiving some ADAS instruction at their vehicle dealership, but only 16.6% indicated it was formal. Participants who received formalized training reported it to be significantly more effective than those who received informal overviews of their systems. Use of trial and error and the owner’s manual were the most frequently reported methods of learning outside of dealership training. Responses indicated that the lack of content tailored to trim-specific vehicle features in owner’s manuals was a barrier to effective use.     

Consumer knowledge and acceptance of advanced driver assistance systems

This research aimed to examine how personal car users are informed about Advanced Driver Assistance Systems (ADAS) in Australia, and the extent to which information received at point of sale influenced the level of understanding and adaptation of ADAS. Further, this study applied the Technology Acceptance Model (TAM) to assess drivers’ intentions to use ADAS within the next month. Participants were required to have purchased a new or second-hand vehicle within the past five years and which had at least one of the following five ADAS: autonomous emergency braking, lane departure warning, adaptive cruise control, blind spot monitoring, and rear cross traffic alert. Participants (N = 217, Mage = 47.87 years, 149 male) completed a 20-minute online questionnaire. The findings revealed that 122 (56%) participants reported not seeking out any information about ADAS prior to purchasing their vehicle. Further, the most reported approaches used by participants to learn about their vehicle’s ADAS were through the owner’s manual (n = 121, 55%) and via trial and error (n = 117, 54%). It was also found that total time spent with the salesperson in explaining ADAS features did not influence drivers’ level of understanding of ADAS or number of days to adapt to the ADAS in their vehicle. However, and consistent with the TAM, perceived usefulness and perceived ease of use were significant positive predictors of intentions to use ADAS within the next month. It was also found that participants who learnt about these technologies either via reading the owner’s manual, trial and error, or a verbal explanation at point of sale appeared to be more frequently classified as reporting higher acceptance of ADAS than those participants who did not use those learning methods. Given that technology in vehicles will continue to advance, it is vital that more work is conducted to educate drivers about the functionality of ADAS. Further, it is also important to communicate where buyers of personal cars can go and find accurate and reliable information about ADAS.     

The impact of smart driving aids on driving performance and driver distraction

In-vehicle information systems (IVIS) have been shown to increase driver workload and cause distraction, both of which are causal factors for accidents. This simulator study evaluates the impact that two prototype ergonomic designs for a smart driving aid have on workload, distraction and driving performance. Scenario complexity was also manipulated as an independent variable. Results showed that real-time delivery of smart driving information did not increase driver workload or adversely affect driver distraction, while also having the positive effect of decreasing mean driving speed in both the simple and complex driving scenarios. Subjective workload was shown to increase with task difficulty, as well as revealing important differences between the two interface designs. The findings are relevant to the development and implementation of smart driving interface designs in the future.     

Hybrid route choice model incorporating latent cognitive effects of real-time travel information using physiological data

The proliferation of information systems is enabling drivers to receive en route real-time travel information, often from multiple sources, for making informed routing decisions. A robust understanding of route choice behavior under information provision can be leveraged by traffic operators to design information and its delivery systems for managing network-wide traffic. However, most existing route choice models lack the ability to consider the latent cognitive effects of information on drivers and their implications on route choice decisions. This paper presents a hybrid route choice modeling framework that incorporates the latent cognitive effects of real-time information and the effects of several explanatory variables that can be measured directly (i.e., route characteristics, information characteristics, driver attributes, and situational factors). The latent cognitive effects are estimated by analyzing drivers’ physiological data (i.e., brain electrical activity patterns) measured using an electroencephalogram (EEG). Data was collected for 95 participants in driving simulator experiments designed to elicit realistic route choices using a network-level setup featuring routes with different characteristics (in terms of travel time and driving environment complexity) and dynamic ambient traffic. Averaged EEG band powers in multiple brain regions were used to extract two latent cognitive variables that capture driver’s cognitive effort during and immediately after the information provision, and cognitive inattention before implementing the route choice decision. A Multiple Indicators Multiple Causes model was used to test the effects of several explanatory factors on the latent cognitive variables, and their combined impacts on route choice decisions. The study results highlight the significant effects of driver attributes and information characteristics on latent cognitive effort and of route characteristics on latent cognitive inattention. They also indicate that drivers who are more attentive and exert more cognitive effort are more likely to switch from their current route by complying with the information provided. The study insights can aid traffic operators and information service providers to incorporate human factors and cognitive aspects while devising strategies for designing and disseminating real-time travel information to influence drivers’ route choices.     

Psychometric properties of driver self-image inventory for Chinese drivers and its associations with personality and driving style

This study aimed to adapt the Driver Self-image Inventory (DSII, Taubman-Ben-Ari, 2008) to Chinese drivers and examine its relationship with personality traits and driving style. Six hundred forty drivers aged 18–55 years agreed to participate in this study. Measurements included the DSII, a personality scale and a validated Chinese version of the Multidimensional Driving Style Inventory (MDSI). The results of exploratory factor analysis (n = 302) and confirmatory factor analysis (n = 305) yielded a three-factor scale with satisfactory reliability. Significant gender differences were found on the DSII factors, with male drivers scoring higher on the impulsive driver factor and lower on the cautious driver factor than female drivers. The validity of the DSII was supported by significant associations between the DSII factors and personality traits, driving style and number of traffic violations and accidents in the previous 12 months. Moreover, drivers with traffic accidents scored significantly lower on the cautious driver factor and higher on the impulsive driver factor than those without traffic accidents. These findings indicate that the reliability and validity of the Chinese version of the DSII are acceptable.     

A comparison of computational driver models using naturalistic and test-track data from cyclist-overtaking manoeuvres

The improvement of advanced driver assistance systems (ADAS) and their safety assessment rely on the understanding of scenario-dependent driving behaviours, such as steering to avoid collisions.This study compares driver models that predict when a driver starts steering away to overtake a cyclist on rural roads. The comparison is among four models: a threshold model, an accumulator model, and two models inspired by a proportional-integral and proportional-integral-derivative controller. These models were tested and cross-applied using two different datasets: one from a naturalistic driving (ND) study and one from a test-track (TT) experiment. Two perceptual variables, expansion rate (the horizontal angular expansion rate of the image of the lead road user on the driver’s retina) and inverse tau (the ratio between the image’s expansion rate and its horizontal optical size), were tested as input to the models. A linear cost function is proposed that can obtain the optimal parameters of the models by computationally efficient linear programming.The results show that the models based on inverse tau fitted the data better than the models that included expansion rate. In general, the models fitted the ND data reasonably well, but not as well the TT data. For the ND data, the models including an accumulative component outperformed the threshold model. For the TT data, due to the poorer fit of the models, more analysis is required to determine the merit of the models. The models fitted to TT data captured the overall pattern of steering onsets in the ND data rather well, but with a persistent bias, probably due to the drivers employing a more cautious strategy in TT.The models compared in this paper may support the virtual safety assessment of ADAS so that driver behaviour may be considered in the design and evaluation of new safety systems.     

Eye movements predict driver reaction time to takeover request in automated driving: A real-vehicle study

For automated driving at SAE level 3 or lower, driver performance in responding to takeover requests (TORs) is decisive in providing system safety. A driver state monitoring system that can predict a driver’s performance in a TOR event will facilitate a safer control transition from vehicle to driver. This experimental study investigated whether driver eye-movement measured before a TOR can predict driving performance in a subsequent TOR event. We recruited participants (N = 36) to obtain realistic results in a real-vehicle study. In the experiment, drivers rode in an automated vehicle on a test track for about 32 min, and a critical TOR event occurred at the end of the drive. Eye movements were measured by a camera-based driver monitoring system, and five measures were extracted from the last 2-min epoch prior to the TOR event. The correlations between each eye-movement measure and driver reaction time were examined, and a multiple regression model was built using a stepwise procedure. The results showed that longer reaction time could be significantly predicted by a smaller number of large saccades, a greater number of medium saccades, and lower saccadic velocity. The implications of these relationships are consistent with previous studies. The present real-vehicle study can provide insights to the automotive industry in the search for a safer and more flexible interface between the automated vehicle and the driver.     

Challenges for automated vehicle driver training: A thematic analysis from manual and automated driving

Considerable research and resources are going into the development and testing of Automated Vehicles. They are expected to bring society a huge number of benefits (such as: improved safety, increased capacity, reduced fuel use and emissions). Notwithstanding these potential benefits, there have also been a number of high-profile collisions involving Automated Vehicles on the road. In the majority of these cases, the driver’s inattention to the vehicle and road environment was blamed as a significant causal factor. This suggests that solutions need to be developed in order to enhance the benefits and address the challenges associated with Automated Vehicles. One such solution is driver training. As drivers still require manual driving skills when operating Automated Vehicles on the road, this paper applied the grounded theory approach to identify eight “key” themes and interconnections that exist in current manual vehicle driver training. These themes were then applied to the limited literature available on Automated Vehicle driver training, and a ninth theme of trust emerged. This helped to identify a set of training requirements for drivers of Automated Vehicles, which suggests that a multifaceted approach (covering all nine themes and manual and Automated Vehicle driving skills) to driver training is required. This framework can be used to develop and test a training programme for drivers of Automated Vehicles.     

A multi-modal approach to cognitive training and assistance in minimally invasive surgery

Minimally-invasive surgery (MIS) offers many benefits to patients, but is considerably more difficult to learn and perform than is open surgery. One main reason for the observed difficulty is attributable to the visuo-spatial challenges that arise in MIS, taxing the surgeons’ cognitive skills. In this contribution, we present a new approach that combines training and assistance as well as the visual and the auditory modality to help surgeons to overcome these challenges. To achieve this, our approach assumes two main components: An adaptive, individualized training component as well as a component that conveys spatial information through sound. The training component (a) specifically targets the visuo-spatial processes crucial for successful MIS performance and (b) trains surgeons in the use of the sound component. The second component is an auditory display based on a psychoacoustic sonification, which reduces and avoids some of the commonly experienced MIS challenges. Implementations of both components are described and their integration is discussed. Our approach and both of its components go beyond the current state of the art in important ways. The training component has been explicitly designed to target MIS-specific visuo-spatial skills and to allow for adaptive testing, promoting individualized learning. The auditory display is conveying spatial information in 3-D space. Our approach is the first that encompasses both training for improved mastery and reduction of cognitive challenges in MIS. This promises better tailoring of surgical skills and assistance to the needs and the capabilities of the surgeons and, thus, ultimately, increased patient safety and health.     

Effects of a driver assistance system with foresighted deceleration control on the driving performance of elderly and younger drivers

It is imperative to enhance the safety of elderly individuals on the roads to ensure the quality of their daily life. Near-miss incidents or accidents at blind intersections often result from a conflict between the behaviors of the driver and of other road users (pedestrians and cyclists). The failure to search for potential conflict in the context of blind intersections is a concern pertaining to road safety. The proposed assistance system performs a proactive braking intervention to achieve a referenced velocity in uncertain situations, such as one in which an unobserved pedestrian might initiate a road crossing. The proactive braking intervention attempts to manage the potential risk of crashing with respect to covert hazards. Because an automated system may impair a human’s ability to perceive and respond to hazardous situations while driving, this study was designed to examine the effects of proactive braking intervention and visual support cues on elderly and younger drivers’ ability to respond to information about potentially hazardous situations. We conducted a public-road driving experiment involving 108 elderly and younger drivers from two non-overlapping age groups. It was observed that the vehicle slowdown realized through the proactive braking intervention enabled the drivers to perform safety confirmation near blind spots and caused them to be more sensitive to and wary of potential hazards. This approach could be effective not only for elderly drivers, but also for young or inexperienced ones.     

The impact of navigation system display size and environmental illumination on young driver mental workload

Driver distraction is a major cause of road crashes and has a great influence on road safety. In vehicles, one of the common distracting sources is navigation systems (NSs). The navigation system (NS) can distract the driver due to following directions and reading the provided information through its display. These tasks take the driver’s attention from the primary task of driving and may cause poor driving performance, increasing the risk of crashes. In this paper, the effect of the environment (i.e., urban areas and rural areas), the navigation system display (NSD) size, environmental illumination, and gender on young drivers between the ages of 18 and 29 years mental workload was investigated using a simulated driving experiment. To evaluate each driving condition, the NASA-TLX (NASA Task Load Index) workload assessment tool, and a distraction evaluation element, were introduced and used to assess the overall workload, the workload subscales and the distraction by the NSD. The assessment showed a higher perceived overall workload for urban areas and night driving as compared to a rural areas and daytime driving. Moreover, the results showed a greater perceived distraction by the NSD in urban areas compared to driving in rural areas. The subjects also felt distracted when using the small NS compared to using the large NS. The study concluded that urban areas driving, and night driving creates higher perceived workload than rural areas and daytime driving. Furthermore, small NSD leads to more perceived distraction than large NSD while driving. The NSD designers may utilize this research findings to optimize NSD designs to improve driving safety, performance and comfort. Moreover, this study contributes to our understanding of the effect of the NSD size on driving workload and distraction.     

The electric vehicle: A new driving experience involving specific skills and rules

The arrival of the electric vehicle (EV) on the market is one consequence of government measures to improve air quality and reduce CO² emissions. However, the EV has specific properties of use associated with its limited range and relative silence compared to normal vehicles, influencing the mobility behaviours of drivers and requiring them to develop some new driving abilities. This paper examines the behavioural modifications brought about by daily use of an electric vehicle at three different levels of driving activity: strategic, tactical and operational. The study collected and analyzed the self-reported behaviours (via questionnaires and travels dairies) of 36 Parisian private drivers, each of whom drove for six months an electric MINI E prototype. The results of the study show that driving an EV requires a learning phase to acquire the skills and knowledge necessary to operate the vehicle. At the strategic level of driving, drivers take into account the restricted range of the EV, implement a daily charge process, and develop new behaviours related to trip planning. The study also examines driver behaviour at the tactical level, in terms of driver interactions with other road users to deal with the silent nature of the EV, and at the operational level of driving, in terms of braking behaviour to master the regenerative braking function of the EV. The paper discusses the interactions between these three levels of driving activity.     

Field operational test of a seatbelt reminder system: Effects on driver behaviour and acceptance

The long-term effects on driver behaviour and acceptance of a seatbelt reminder system were examined in an on-road study. The system was capable of detecting seatbelt use in all seating positions and produced a two-stage visual and auditory warning if occupants were unrestrained. The effects of this system were evaluated alone and in combination with two other intelligent transport systems: intelligent speed adaptation and a following distance warning system. Twenty-three fleet car drivers drove an instrumented vehicle (SafeCar) for at least 16,500 km as part of their everyday driving. The results revealed that driver and passenger interaction with the seatbelt reminder system led to large and significant decreases in the percentage of trips where occupants were unbelted, in the percentage of total driving time spent unbelted, and in the time taken to fasten a seatbelt in response to system warnings. The seatbelt reminder system was rated by drivers as being useful, effective and socially acceptable, and led to a decrease in drivers’ subjective workload. These results were found even though the baseline pre-exposure seatbelt wearing compliance rates among participants were high.     

Evaluating the impact of adaptive signal control technology on driver stress and behavior using real-world experimental data

While the operational and crash reduction benefits of adaptive signal control technology (ASCT) have long been investigated, the impact of this technology on driver behavior and stress is still uncertain. This study evaluated the impact of ASCT on driver behavior and stress in a real-world environment. Participants travelled through two arterial corridors, one equipped with ASCT and the other having traditional time-of-day coordinated signals. Driver stress was measured using a heart rate detector and a perceived stress scale while driver behavior was examined using vehicular trajectory data. Overall, driving behavior improved on the ASCT as compared to the non-ASCT corridor, as indicated by higher speeds and a fewer number of stops on the ASCT corridor relative to the non-ASCT corridor. Repeated measures ANOVA showed a statistically significant reduction in driver heart rate by −10 beats per minute over the ASCT corridor. A similar trend was observed for drivers’ perceived stress, analyzed by Wilcoxon sign ranked test. Driving behavior also showed significant improvement with ASCT presence, and speed was found to be negatively correlated with stress. Furthermore, the participants’ speed was controlled by the two systems i.e. ASCT and non-ASCT as expected. This study provides a significant proof of concept that ASCT can create positive improvements in driver stress and behavior that can be further investigated in the future.     

Emotion development system by interacting with human EEG and natural scene understanding

In this paper, we propose a mental development system for understanding the emotional status of humans, and sharing emotions with human subjects. According to the relationship between emotional factors and characteristics of an image, we incorporate the fuzzy concept to extract emotional features using L^*C^*H^* color and orientation information. On the other hand, we also consider the EEG signals which are stimulated by natural stimuli to form the semantic emotional features as well. Emotionally relevant features are firstly clustered into two categories with degrees of belongingness to each cluster to initialize the membership functions of a neuro-fuzzy system. The IF-THEN rules of a neuro-fuzzy system to understand the positive and negative human emotions will be constructed by interacting with human. Then the system attempts to extend the number of understandable emotion. Through the time, the system sub-clusters the emotional features so that the number of membership function of the neuro-fuzzy network will increase to incorporate more complicated human expertise considering more human emotions. Using such a developmental process, the proposed system can develop a mental ability to understand more complex human emotions by mining the characteristics of emotional features and interacting with its environment.     

TPS: A hybrid automatic-interactive system for developing proofs

The theorem proving system Tps provides support for constructing proofs using a mix of automation and user interaction, and for manipulating and inspecting proofs. Its library facilities allow the user to store and organize work. Mathematical theorems can be expressed very naturally in Tps using higher-order logic. A number of proof representations are available in Tps, so proofs can be inspected from various perspectives.     

A cognitive-emotional model of NSSI: using emotion regulation and cognitive processes to explain why people self-injure

Non-suicidal self-injury (NSSI) is a complex behaviour, routinely engaged for emotion regulatory purposes. As such, a number of theoretical accounts regarding the aetiology and maintenance of NSSI are grounded in models of emotion regulation; the role that cognition plays in the behaviour is less well known. In this paper, we summarise four models of emotion regulation that have repeatedly been related to NSSI and identify the core components across them. We then draw on social cognitive theory to unite models of cognition and models of emotion in developing a new cognitive-emotional model of NSSI. Our model articulates how emotion regulation and cognition can work in concert to govern NSSI, and offers several new research questions that can be addressed within this framework.     

A study of the compliance level of connected vehicle warning information in a fog warning system based on a driving simulation

The accidents in the freeway are frequent and more serious on foggy days. Connected vehicle (CV) technology as a new technology can inform drivers of fog conditions in advance so the drivers can adjust their driving behaviors through Human Machine Interface (HMI) and Dynamic Message Sign (DMS) in the fog warning system. The level of driver's compliance with a fog warning system is the key to assessing the effectiveness of the fog warning system. To evaluate the compliance level of fog warning system, the study established a CV system testing platform based on driving simulator, and analyzed the changes in driving behavior and influencing factors for drivers under three different visibility conditions (No fog, light fog and heavy fog) based on the platform, finally, comprehensive evaluation of the optimal fog warning system under different foggy conditions based on the compliance level were made. Research indicators are divided into three aspects: 1) the response degree, including the mean speed, the minimum speed in the fog zone, the difference of the speeds when entering and leaving the fog zone, the proportion of speed following, and the speed reduction proportion prompt; 2) the response start time, which is the time to start slowing down to the speed limit; and 3) the response time duration, namely, the time in which the speed limit is followed. The results show that the response degree of the driver to the fog warning system is high, the fog warning system can effectively reduce the driving speed of the drivers and improve the speed following proportion of the drivers, the deceleration ratio of the driver at each warning point is relatively high, and the influencing factors of various indicators are complex but are mostly related to visibility and technical level. Comprehensive evaluation results show that the warning mode of the combined HMI and DMS has the highest level of compliance under light fog conditions, and when fog concentration increases, compliance level of fog warning system with HMI only is higher than others. The study establishes a reference platform for CV system and provides methods and index system for the compliance level of CV research.     

Human-machine interaction: A case study on fake news detection using a backtracking based on a cognitive system

Although the Internet provides a variety of news, it also can give confusion caused by personal subjective thoughts such as personal TV, blogs, and unproven news. The unproven news is written in a subjective direction with added personal opinions rather than objective content, so readers may acquire knowledge with the wrong outlook. In addition, fake news is being produced and the problem of social polarization is becoming serious. In the end, it is necessary to detect the fake news, but it is not easy to distinguish the truth of published news because of the lack of fake news distinction time compared to the speed of information sharing on the Internet and the diversity and strong subjectivity of news. Therefore, in this paper, the possibility of fake news is defined by using the reverse-tracking method of the articles which are posted on the Cognitive System. Finally, as the result, the detection rate is average 85%.     

Justice and the human alarm system: The impact of exclamation points and flashing lights on the justice judgment process

Extending theory within the justice domain and work on the human alarm system, the current paper argues that the process by which justice judgments are formed may be influenced reliably by the activation of psychological systems that people use to detect and handle alarming situations. Building on this analysis, it is further proposed that if this line of reasoning is true then presenting alarm-related stimuli, such as exclamation points and flashing lights, to people should lead to more extreme judgments about subsequent justice-related events than not presenting these alarming stimuli. Findings collected using different experimental paradigms provide evidence supporting these predictions both inside and outside the psychology lab. Implications for the social psychology of justice and the human alarm system literature are discussed.