Driving Simulator Experiment Research Articles

Game theory-based mandatory lane change model in intelligent connected vehicles environment

In the environment of intelligent connected vehicles, drivers are capable of making wiser and safer decisions. However, the interaction between drivers and vehicle systems has undergone changes in the intelligent connected vehicles environment, leading to a decrease in the applicability of existing microscopic driving models, such as the mandatory lane change model, which requires reevaluation or improvement. Therefore, to investigate the influence of different intelligent connected vehicles environments on the microscopic mandatory lane-changing model, this study developed three interaction systems to characterize different intelligent connected vehicles environments: the baseline, warning group, and guidance group. The Baseline provides basic information, the warning group adds icons of preceding vehicles and real-time headway information, while the guidance group further includes speed and voice guidance functions. The baseline describes the traditional environment, while the other two groups describe the intelligent connected vehicles environment. Using a self-developed intelligent connected vehicle testing platform, we conducted driving simulation experiments with 43 participants at the interchange merging area of a highway. This study, grounded in game theory, establishes function models for participants, strategies, and payoff functions in the mandatory lane-changing process. Utilizing data from driving simulation experiments, the parameters of the dual-layered planning model are calibrated. Evaluation of the constructed model is conducted through confusion matrices and lane-changing spatiotemporal characteristic indicators. The results demonstrate satisfactory predictive performance of the baseline group model, warning group model, and guidance group model across different intelligent connected vehicles environments. Specifically, compared to existing literature, the baseline group model exhibits improvements of 7 % and 2 % respectively in overall lane-changing detection accuracy by drivers. The warning group model shows improvements of 2.9 % and 1.7 %, while the guidance group model exhibits improvements of 5.1 % and 4.3 %. Additionally, the baseline group model reduces the mean absolute error in predicting different game strategies by 16.7 % and 5.6 % respectively compared to existing literature. Concerning lane-changing position prediction, the warning and guidance group models demonstrate minimal errors, whereas the baseline group model exhibits good consistency in predicting lane-changing duration. Furthermore, both the warning and guidance group models show some delay in predicting lane-changing duration. While intelligent connected vehicles environments significantly influence the prediction of lane-changing positions, they do not significantly affect the prediction of lane-changing duration. However, game strategies significantly impact the prediction of lane-changing duration but do not significantly affect the prediction of lane-changing positions. The study findings offer valuable insights into micro lane-changing behaviors of drivers in intelligent connected vehicles environments, bearing crucial significance for the in-depth investigation of real-time control and guidance strategies for vehicles in merge areas of highways under intelligent connected vehicles conditions.

Safety evaluation and prediction of takeover performance in automated driving considering drivers’ cognitive load: A driving simulator study

Automated vehicles alleviate the need for driver attention and control. However, a takeover request (TOR) to the driver remains essential for emergencies and safety–critical scenarios beyond automation's capability. Thus, accessing drivers’ safety performance in various TOR scenarios is crucial for conditionally automated driving (SAE L3). However, TOR safety performance is seldom examined concerning drivers' cognitive load, despite its presumed relevance to TOR scenarios and human–machine interaction. Moreover, the adequacy of the time window preceding a TOR, critical for TOR safety prediction, remains inadequately explored. This study aims to assess safety performance across diverse TOR scenarios in Level 3 conditional automation, incorporating drivers’ cognitive load, and predict TOR safety by considering the time window's impact. A driving simulator experiment gathered eye movement and driving behavior data from 37 recruited participants. Participants were instructed to take control of the vehicle from automated driving within a time budget (TB) of 3 s or 7 s in obstacle avoidance (OA) or lane keeping (LK) scenarios while engaging in non-driving-related tasks (NDRTs). Participants’ subjective cognitive load in the different TOR scenarios was scaled using NASA-TLX. Furthermore, safe TOR performance was predicted utilizing Convolutional Neural Networks (CNNs) across different time window sizes preceding a TOR. The results indicate that: 1) cognitive loads in takeover scenarios ranked from highest to lowest as TB = 3s_OA, TB = 3s_LK, TB = 7s_OA, TB = 7s_LK; and the cognitive loads of the NDRTs ranked from highest to lowest as mistake finding, texting, chatting, monitoring; 2) the takeover safety performance in the four scenarios from lowest to highest was TB = 3s_OA, TB = 3s_LK, TB = 7s_OA, TB = 7s_LK; likewise, the takeover safety performance during the four NDRTs ranked from lowest to highest as mistake finding, monitoring, texting, chatting; 3) the time window size before the TORs significantly affected the prediction performance of the model. A 30-second window was recommended as optimal for predicting takeover safety using the CNN model, achieving an average F1 score of 0.8120 and 81.98 % accuracy. This study's findings enhance our comprehension of driving behavior characteristics during TOR and offer valuable insights for detecting driver states in conditionally automated driving contexts.

Exploring the associations of demographics and scale measures with cognitive driving behavior among older drivers in China

Age-related changes and frailty are reasons for the high proportion of older drivers in certain types of crashes, such as giving right of way at intersections and turning left. The identified crash causes include the driver’s demographics, driving style, cognitive function, and mental workload. This study aimed to explore the associations of demographics and scale measures with cognitive driving behavior. Thirty-nine drivers, consisting of twenty younger drivers (18–60 years old) and nineteen older drivers (above 60 years old), participated in driving simulation experiments after completing scale tests. The selected scale measures included the demographic questionnaire, Multidimensional Driving Style Inventory (MDSI-C), Mini-Mental State Examination (MMSE), Trail Making Test Part A (TMT-A) and Part B (TMT-B), and the National Aeronautics and Space Administration Task Load Index (NASA-TLX) for obtaining subjective information from drivers. Driving scenarios were developed based on the driving characteristics of older adults to investigate age-related driving ability. The driving behavior parameters included reaction time, lateral stability, and driving speed, corresponding to reaction, perception, and execution. Three stepwise regression models showed that NASA-TLX, the interaction between age and driving experience, and the interaction between age and TMT-A significantly explained 53.3 % of reaction time variance; TMT-A, risk driving style, anxiety driving style, and gender significantly explained 53.5 % of lateral stability variance; TMT-A, NASA-TLX, and MMSE significantly explained 60.6 % of driving speed variance. Subsequently, the impact of four age-related predictor variables on driving behavior was further discussed. It is worth noting that a rich driving experience may compensate for driving performance. However cognitive impairment impairs this compensation. Driving behavior is influenced by a combination of various factors. Age, as a physiological indicator, is not sufficient to be a strong predictive factor for lateral stability and driving speed. The results provide a reference for traffic safety management departments to streamline driving suitability test procedures and propose targeted training methods for older drivers.

Effects of daylight and darkness at daytime versus nighttime on driver sleepiness: A driving simulator study

The study explores the impact of light conditions on driver sleepiness. In a driving simulator experiment, 20 drivers drove both during daytime in an alert condition and then later at night after being awake since early morning. Light conditions were manipulated by driving in a simulated nighttime scenario in a dark room (1 lx) versus driving in simulated daylight in a room lit with light emitting diodes combining blue light with a yellow phosphor giving a two peaked spectrum (212 lx). Both the daylight and the darkness scenarios were driven daytime and nighttime in a 2 × 2 design. Sleepiness was measured during the four 1-hour drives in terms of subjective sleepiness ratings, divided attention ability, driving performance, heart rate variability and blink behaviour. Significant differences were found in all measured sleepiness indicators between the daytime and nighttime drives, and in most indicators for time-on- task. No significant main effects were found between simulated daylight and darkness. A psychomotor vigilance test conducted before and after each drive also showed no significant effects for lighting condition. Further research, preferably using longitudinal studies in more realistic settings on real roads, is needed to determine which behaviours and which cognitive processes that are affected when driving in daylight versus darkness.

Modeling the influence of connected vehicles on driving behaviors and safety outcomes in highway crash scenarios across varied weather conditions: A multigroup structural equation modeling analysis using a driving simulator experiment

Equipped with advanced sensors and capable of relaying safety messages to drivers, connected vehicles (CVs) hold the potential to reduce crashes. The goal of this study is to assess the impacts of CV technologies on driving behaviors and safety outcomes in highway crash scenarios under diverse weather conditions, including clear and foggy weather. A driving simulator experiment was conducted and the multigroup structural equation modeling (SEM) was employed to explore the complex interrelationships between the propensity of traffic conflicts, utilization of CV alerts, weather, psychological factors, driving behaviors, and other relevant variables for two different crash locations, namely a straight section and a horizontal curve. Two latent psychological factors including aggressiveness and unawareness were constructed from driving behavior as vehicles passed by crash scenes such as brake, throttle, steering angle, lane offset, and yaw. The SEM can measure latent psychological factors and model interrelationships concurrently through a single statistical estimation procedure. Results of the multigroup SEM showed that CV alerts could significantly reduce the unawareness on a horizontal curve and thus lower the propensity of traffic conflicts. Additionally, the overall effect of foggy weather on conflicts was found to be positive on a horizontal curve, despite the potential benefit of improving situational awareness. In contrast, the single group SEM failed to reveal any significant interrelationships in its structural model by pooling data from both crash locations. The obtained insights can guide the development of driving assistance systems, highlighting the necessity of customization considering weather conditions and location-specific factors.

Behavioral Patterns of Drivers under Signalized and Unsignalized Urban Intersections

Under the general trend of mixed traffic flow, an in-depth understanding of the driving behaviors of traditional vehicles is of great significance for the design of autonomous vehicles and the improvement in the safety and acceptance of autonomous vehicles. This study first obtained microdata on the behaviors of drivers through driving simulation experiments and conducted research in stages. Then, generalized linear mixed-effects models were constructed to study the main effects and interaction effects of driver attributes and traffic conditions on driving behaviors. The data analysis shows that the overall speed of drivers passing through intersections follows a “deceleration acceleration” mode, but the fluctuations are more pronounced at signalized intersections, and the signal control significantly changes the position of the lowest speed when turning left. According to the different signal control and driving tasks, there are significant differences in a driver’s acceleration patterns between the entry and exit stages. A driver’s heart rate fluctuates greatly during the exit phase, especially during straight tasks. Compared with other indicators, the change in the gaze duration is not significant. In addition, interaction effects were observed between driver attributes and traffic conditions, with participants exhibiting different behavioral patterns based on their different attributes. The research results can provide a basis for the design of driving assistance systems and further improve the interactions between autonomous vehicles and traditional vehicles at intersections.

Effectiveness of unconventional anti-texting messages displayed on dynamic message signs

ABSTRACT Dynamic message signs (DMS) are widely adopted to display traffic and safety information. This study investigated the effectiveness of behavioral safety messages, including 10 unconventional anti-texting messages used on DMS. Feedback on the usability and effectiveness of various messages was collected from 120 drivers in an online survey. The influence of ten unconventional anti-texting DMS messages on driver performance was observed using driving simulator experiments with sixty participants. 89% of the respondents reported reading the signs, while 85% thought about the relevance of displayed messages. During the simulator experiment, drivers responded to a significantly lesser number of text messages after observing the anti-texting DMS messages, compared to the segments without any DMS. Both self-perceived opinions and driver performance metrics indicated a positive influence of displaying safety messages on DMS. Group comparisons across age and gender showed that some messages were more effective than others among specific driver groups.

Construction and Analysis of Driver Takeover Behavior Modes Based on Situation Awareness Theory

In automated driving, takeover behavior is crucial to safe transition of control. Based on this fact, the goal of the present study is to construct the driver's takeover behavior mode and verify its effectiveness. In the takeover process, we define the takeover behavior mode, which is the inherent mode of completing the takeover task, as the behavioral theoretical framework that drivers should follow when taking over. We conducted a driving simulation experiment to analyze driver takeover behavior in the fog zone, where non-driving-related tasks were external variables. First, we constructed the takeover behavior mode (situation awareness - decision and reaction - takeover performance). Second, we analyzed the characteristics of each stage and the correlation between each stage of the takeover behavior mode accordingly. The result shows that drivers' performance in each stage differed significantly under different non-driving-related tasks. For example, their saccade and gaze duration were longer on the entertainment task than on the work task, and the driver prioritized the longitudinal stability after taking control of the automated vehicle. Results show that there is a correlation between different stages of takeover behavior mode. In this study, we propose the takeover behavior mode, aiming to help beginners and researchers in this field understand the driver takeover process more intuitively and provide behavioral theoretical support for subsequent studies. In addition, the takeover behavior mode can provide support for training drivers in real-world driving situations.

Optimization of colored pavement considering driving behavior and psychological characteristics under dynamic low-visibility conditions related to fog—a driving simulator study

Objective Colored pavement is commonly used to reduce the road traffic risk and promote road traffic safety, but its performance in foggy environments has not been fully assessed. The goal of this research is to explore the effectiveness and optimization of colored pavement in a dynamic low-visibility environment. Methods A driving simulation experiment is conducted. Three road risk sections in which collisions are common, including a long straight section, a sharp bend section, and a long downslope section, are considered, and three forms of colored pavement are used in five different visibility environments. The effectiveness of the colored pavement is explored by collecting and analyzing driving behavior and physiological characteristic data for 30 drivers in the established driving environment, and information is obtained through a subjective colored evaluation questionnaire. Eight evaluation indexes are selected from the perspectives of driving behavior and physiological characteristics, and the gray premium evaluation method is applied to evaluate the effectiveness of different forms of colored pavement considering the influence of visibility. Finally, the optimal colored pavement under various visibility and road alignment conditions is proposed. Results The results show that reasonably selecting colored pavement can effectively improve drivers’ behaviors and physiological characteristics under foggy conditions. For different road alignments and visibility conditions, different forms of colored pavement should be used to ensure road traffic safety. Conclusions The findings provide a theoretical reference for the optimization of colored pavement in foggy conditions.

Nudging human drivers via implicit communication by automated vehicles: Empirical evidence and computational cognitive modeling

Understanding behavior of human drivers in interactions with automated vehicles (AV) can aid the development of future AVs. Existing investigations of such behavior have predominantly focused on situations in which an AV a priori needs to take action because the human has the right of way. However, future AVs might need to proactively manage interactions even if they have the right of way over humans, e.g., a human driver taking a left turn in front of the approaching AV. Yet it remains unclear how AVs could behave in such interactions and how humans would react to them. To address this issue, here we investigated behavior of human drivers (N = 19) when interacting with an oncoming AV during unprotected left turns in a driving simulator experiment. We measured the outcomes (Go or Stay) and timing of participants’ decisions when interacting with an AV which performed subtle longitudinal nudging maneuvers, e.g. briefly decelerating and then accelerating back to its original speed. We found that participants’ behavior was sensitive to deceleration nudges but not acceleration nudges. We compared the obtained data to predictions of several variants of a drift-diffusion model of human decision making. The most parsimonious model that captured the data hypothesized noisy integration of dynamic information on time-to-arrival and distance to a fixed decision boundary, with an initial accumulation bias towards the Go decision. Our model not only accounts for the observed behavior but can also flexibly generate predictions of human responses to arbitrary longitudinal AV maneuvers, and can be used for both informing future studies of human behavior and incorporating insights from such studies into computational frameworks for AV interaction planning.

Effects of In-Vehicle Touchscreen Location on Driver Task Performance, Eye Gaze Behavior, and Workload During Conditionally Automated Driving: Nondriving-Related Task and Take-Over.

This study investigated the effects of nondriving-related task (NDRT) touchscreen location and NDRT difficulty level on the driver task performance, eye gaze behavior, and workload during SAE Level 3 conditionally automated driving. Two driver tasks were considered: a visuomanual NDRT and a take-over task. Touchscreens are expected to play important roles inside automated vehicles. However, few studies have investigated the driver-touchscreen interaction during automated driving. A driving simulator experiment was conducted. The experimental task consisted of two successive subtasks: an NDRT followed by a take-over task. NDRT touchscreen location (Upper Left, Upper Right, and Lower Right) and NDRT difficulty level (Easy and Hard) were the independent variables. A set of driver task performance, eye gaze behavior, and perceived workload measures were employed for each subtask as the dependent variables. NDRT touchscreen location significantly affected both the NDRT and the take-over task performance. Lower Right was superior to Upper Right in the NDRT performance but was inferior in the take-over task performance. NDRT touchscreen location affected the perceived physical workload of the NDRT. NDRT difficulty level affected the perceived workload of the take-over task. The research findings enhance our understanding of how NDRT touchscreen location and NDRT difficulty level impact the driver task performance during conditionally automated driving, and, further provide useful design implications and knowledge. The study results would inform the NDRT touchscreen interface design and the NDRT design for conditionally automated vehicles.

Effects of driving style on takeover performance during automated driving: Under the influence of warning system factors

Driving style has been proposed to be a critical factor in automated driving. However, the role of driving style in the process of taking over during automated driving needs further investigation. The main purpose of this study was to investigate the influence of driving style on takeover performance under the influence of warning system factors. In addition, this study also explored whether the impact of driving style on reaction time varies over time and the role of driving style on a comprehensive takeover quality indicator. Two driving simulation experiments with different takeover request (TOR) designs were conducted. In experiment 1, content warning information was provided in the TOR with different warning stage designs; in experiment 2, countdown warning information was provided in the TOR with different warning stage designs. Sixty-four participants (32 for experiment 1 and 32 for experiment 2) were classified into two groups based on their driving style (i.e., aggressive, or defensive) using the Chinese version of the Multidimensional Driving Style Inventory (the brief MDSI-C). The results suggested that drivers’ driving style had significant effects on takeover performance, but the effects were influenced by warning system designs. Specifically, defensive participants performed better takeover performance, i.e., shorter reaction time and cautious vehicle control behaviors, than aggressive participants in most warning conditions. The content and countdown warning information and warning stage design affected the roles of driving style on takeover performance: 1) compared to the one-stage warning design, the two-stage warning design significantly shortened the reaction time of the participants with different driving styles, 2) compared to the countdown warning information design, the design of content warning information can shorten the reaction time of aggressive participants and lengthen the reaction time of defensive participants in the two-stage warning conditions, and 3) compared to the content warning information design, countdown warning information can improve the safe takeover performance of defensive participants. This study provides a better understanding of the role of driving style on takeover performance, and driving style should be considered when designing warning systems for autonomous vehicles.

Improving road safety at horizontal curves using V2I speed warning messages

Introduction: According to the Federal Highway Administration, a quarter of fatal collisions has occurred at horizontal curves. The average collision rate at horizontal curves was found to be three times higher than other types of highway segments. The lack of compliance with the speed limit and driver-related factors are among the main contributing factors to those collisions. Vehicle to Infrastructure (V2I) communications can address these limitations by providing drivers with valuable in-vehicle warning messages based on operational and safety data. There is limited effort investigating the impact of different types of V2I warning messages at horizontal curves and among different profiles of drivers. This study aims to thoroughly examine drivers’ behavior and compliance with different V2I warning messages when approaching horizontal curves. Methods: A driving simulator experiment and self-reported survey were conducted. Three main hypotheses were analyzed in this study. First, whether supplying drivers with in-vehicle V2I warning messages will positively affect drivers’ behavior at horizontal curves compared to the standard road signs. Second, whether there will be a significant difference in drivers’ behavior when receiving text and audio V2I warning messages. Third, whether seniors and female drivers will comply more with speed limit advisory provided through V2I message than younger and male drivers. Results: The Analysis of Covariance confirmed the first two hypotheses. Two main measures of drivers’ behavior found to be lower in the V2I communication scenarios compared to the base one. The audio warning message was found to be more promising in increasing drivers’ compliance with speed limit advisory when approaching the curves. Analyzing the third hypothesis revealed that younger and male drivers had higher curve initiation speed compared to females and seniors. Practical Applications: The findings of this study can be used by transportation researchers, authorities, and car manufacturers to improve the effectiveness of in-vehicle V2I warning messages among different profiles of drivers.

A methodology for setting credible speed limits based on numerical analyses and driving simulator experiments

Speed management is an integral part of the Safe System approach and tackling unsafe speeds is the first action to fix a transport system that fails to protect people. There is a consensus that where traffic speeds are a safety issue, lowering the speed limit is considered “reasonable and safe” for conditions. Nevertheless, not only should a speed limit be reasonable and safe, but it should also be credible. Otherwise, that posted speed limit is likely to be ignored. In many instances, speed limits are not credible and highway agencies still need guidance on appropriate procedures to set credible speed limits. The main objective of this study is to propose and test a novel methodology to set credible speed limits, based on the integration of the results achieved by numerical analyses and driving simulator experiments. The proposed methodology is innovative since it takes into consideration both the design characteristics of the road infrastructure according to a specific procedure as well as the drivers’ operating speeds, which are evaluated using the results of both speed prediction models and driving simulator experiments. The methodology was tested to set new speed limits on the A16 Naples–Canosa motorway, section Baiano–Candela, in southern Italy, where a posted speed limit of 80 km/h is installed in both travel directions and a new speed limit of 100 km/h is proposed, based on the results of the experiments developed within the methodology. Since the speed limit selection is associated with the expected crash frequency, the final selection of the speed limit should take into account also a safety impact assessment, considering both the expected change in the speed distribution as well as the effects of the safety countermeasures implemented in association to the speed limit change. In this study, the proposed safety countermeasures are the activation of four sections with point-to-point speed control and targeted measures at 45 curves, consisting of (1) high friction surface treatments, (2) correction of superelevation deficiencies, (3) installation of curve warning signs, chevrons, and sequential flashing beacons, and (4) shoulder rumble strips. The safety impact assessment shows that the increase in the speed limit combined with the implementation of the proposed safety countermeasures allows a crash reduction of 23%. The estimated benefit/cost ratio of the safety countermeasures is 4.66.

Driver’s gaze behaviour before, during and after take-over manoeuvres: Influence of agentivity associated with different automation solutions

In the context of progressive automation of the driving activity, an alternation of automated driving phases and manual driving phases is becoming a reality. The problem of regaining manual control of the vehicle after a period of automated control (i.e., take-over) is critical, particularly concerning visual exploration during the transition phase. A driving simulator experiment was designed to investigate the impact of the level of agentivity manipulated by different levels of motor engagement on gaze parameters during the different temporal stages of a non-critical take-over situation (i.e., overtaking collision zone). The level of motor engagement decreased according to the increase in the level of automation; eye tracking data were collected, and gaze distribution over functional areas of interest was analysed across several periods of interest. Results revealed an influence of the degree of motor engagement on the gaze parameters linked to the integration and processing of visual information for a nominal driving period (i.e., automation activated) as well as during the take-over preparation period. During the period of effective resumption of manual control, most of the ocular parameters went back to their initial values, except for the higher motor disengagement modes (i.e., lowest levels of agentivity). These automation levels seem to show a residual influence of take-overs on manual driving, particularly in the ocular exploration of areas carrying the information useful for the fine regulation of the vehicle trajectory.

Deep learning assisted three triboelectric driving operation sensors for driver training and behavior monitoring

Improving the driving skills of drivers, particularly during the training stage, is crucial in reducing the likelihood of road traffic accidents. In this work, a driver training assistance system (DTAS) is developed for driver training and behavior monitoring. The DTAS integrates three triboelectric driving operation sensors, including gear shift sensor, steering angle sensor, and pedal sensors. Through the ingenious structural design of contact-separation and freestanding-triboelectric-layer mode, these triboelectric sensors have the characteristics of simple structure, easy manufacture and installation, and self-powered, which avoids the complex wiring problem in the limited space of the vehicle. The basic electrical performance test of triboelectric sensors and driving simulation experiment show that the developed DTAS can monitor the driver behavior and provide the feedback on each driver’s operation process in real-time. Combined with deep learning (DL) technology, the DTAS can identify whether the driving operation of drivers in specific training scenarios is correct or not, with an accuracy rate of 97.5%. This work is aimed at assisting the novice or learner drivers in driving training, which helps to improve their driving skills and form good driving habits. The proposed scheme can provide new ideas for the innovative exploration of driving training modes without coaching.

Assessment and optimization of tunnel lighting quality based on drivers' visual comfort: From methodology to application

Drivers' visual comfort is directly affected by tunnel lighting quality. Currently, many built tunnels have poor-quality lighting with varying degrees of visual interference, which may lead to visual discomfort and traffic accidents. To solve this problem, this study proposes a method to assess and optimize the tunnel lighting quality, aiming to evaluate the lighting quality from the perspective of drivers' visual comfort and improve the lighting effect in each lighting zone of the tunnel. First, the visual characteristics of drivers and quantitative relationship between visual comfort and tunnel lighting environment are derived through field tests and mathematical statistics. Second, the comfort range based on visual characteristic indicators (VCIs) is obtained via a comprehensive analysis of objective physiological responses and subjective questionnaire survey results of drivers, which is used as the comfort criteria to assess the tunnel lighting quality. Subsequently, the effects of luminance, luminance uniformity, and flicker frequency on drivers' visual comfort are considered, and a lighting optimization model based on the adaptive penalty function particle swarm optimization algorithm is established by combining comfort constraints and energy-saving objectives. Finally, driving simulation experiments are performed using virtual reality technology, which verify the effectiveness of the assessment and optimization methods. The results show that drivers' visual comfort in the tunnel threshold zone increases with an increase in the luminance reduction coefficient and decrease in the vehicle speed, and drivers' visual comfort in the tunnel interior zone increases with an increase in the road surface illuminance and visibility. The comfort range of the VCIs classify the tunnel lighting quality into four evaluation levels. Therefore, the proposed method can accurately and quantitatively assess the comfort of the tunnel lighting environment, improving drivers' visual comfort in the tunnel by 48.08 %, while saving 12.03 % of energy.

Safety effect of tunnel environment self-explaining design based on situation awareness

The aim of this study was to investigate the safety effect of environment self-explaining design from drivers’ situation awareness. First, a situation awareness model was developed based on drivers’ situation awareness status and the schema proposed in this study. Subsequently, simulation experiment environments were developed using 3D Max software. A total of 30 driver volunteers were recruited to perform driving simulation experiments under different tunnel environment self-explaining design scenes. Notably, the information on drivers’ eye movement and driving behaviors was collected using eye trackers and a driving simulator. Last, the situation awareness level of drivers was estimated utilizing the situation awareness model. The results indicated that drivers’ situation awareness varied with tunnel environment self-explaining design scenes. The visual illusion marking colored pavement and light-colored decorated sidewall in tunnels provided greater self-explaining performance and significantly enhanced driver cognition of the driving environment. Moreover, the optimal outcomes were achieved, including the scheme of inclined slope pattern, longitudinal marking colored pavement, and blue-white decorated sidewall in tunnels. As revealed by the findings of this study, tunnel environment self-explaining design takes on a critical significance in improving tunnel driving environment and safety.

Driving safety: Investigating the cognitive foundations of accident prevention

Driving is a crucial aspect of personal independence, and accurate assessment of driving skills is vital for ensuring road safety. This study aimed to identify reliable cognitive predictors of safe driving through a driving simulator experiment. We assessed the driving performance of 66 university students in two distinct simulated driving conditions and evaluated their cognitive skills in decision-making, attention, memory, reasoning, perception, and coordination. Multiple regression analyses were conducted to determine the most reliable cognitive predictor of driving outcome. Results revealed that under favorable driving conditions characterized by good weather and limited interactions with other road users, none of the variables tested in the study were able to predict driving performance. However, in a more challenging scenario with adverse weather conditions and heavier traffic, cognitive assessment scores demonstrated significant predictive power for the rate of traffic infractions committed. Specifically, cognitive skills related to memory and coordination were found to be most predictive. This study underscores the significance of cognitive ability, particularly memory, in ensuring safe driving performance. Incorporating cognitive evaluations in driver licensing and education/training programs can enhance the evaluation of drivers' competence and promote safer driving practices.

Latent Hazard Notification for Highly Automated Driving: Expected Safety Benefits and Driver Behavioral Adaptation

Although latent hazard notification for highly automated driving is expected to enhance traffic safety, its practical effects have yet to be verified. This study systemically investigated the expected safety benefits and driver behavioral adaptation based on structural equation modeling. First, we developed a notification system to inform drivers of latent hazards with auditory alerts and conducted a driving simulation experiment involving eyes-off-road situations. To test the system, we adopted two types of events (i.e., the collision avoidance function working or failure) in which latent hazards transform into immediate risks. Then, a measurement model was developed to evaluate driver trust, driver attention, and traffic safety. Subsequently, we examined the corresponding causal relationships. On the one hand, latent hazard notification significantly improves driver attention (i.e., more fixations on latent hazards, less engagement in non-driving-related tasks, and faster notice of immediate risks), which significantly enhances traffic safety. On the other hand, latent hazard notification significantly increases driver trust, which lowers driver attention and consequently impairs traffic safety. This causality reveals driver behavioral adaptation, although driver trust does not directly affect traffic safety. Overall, we find that latent hazard notification for highly automated driving can improve traffic safety, but the consequent driver behavioral adaptation impairs 15.12% of the expected safety benefits.