Driver distraction and its effects on partially automated driving performance: A driving simulator study among young-experienced drivers

Vigilance Decrement During On-Road Partially Automated Driving Across Four Systems

OBJECTIVE

This study uses a detection task to measure changes in driver vigilance when operating four different partially automated systems.

BACKGROUND

Research show temporal declines in detection task performance during manual and fully automated driving, but the accuracy of using this approach for measuring changes in driver vigilance during on-road partially automated driving is yet unproven.

METHOD

Participants drove four different vehicles (Tesla Model 3, Cadillac CT6, Volvo XC90, and Nissan Rogue) equipped with level-2 systems in manual and partially automated modes. Response times to a detection task were recorded over eight consecutive time periods.

RESULTS

Bayesian analysis revealed a main effect of time period and an interaction between mode and time period. A main effect of vehicle and a time period x vehicle interaction were also found.

CONCLUSION

Results indicated that the reduction in detection task performance over time was worse during partially automated driving. Vehicle-specific analysis also revealed that detection task performance changed across vehicles, with slowest response time found for the Volvo.

APPLICATION

The greater decline in detection performance found in automated mode suggests that operating level-2 systems incurred in a greater vigilance decrement, a phenomenon that is of interest for Human Factors practitioners and regulators. We also argue that the observed vehicle-related differences are attributable to the unique design of their in-vehicle interfaces.

A method to enhance drivers' hazard perception at night based on "knowledge-attitude-practice" theory

During nighttime driving, the inherent challenges of low-illuminance conditions often lead to an increased crash rate and higher fatalities by impairing drivers' ability to recognize imminent hazards. While the severity of this issue is widely recognized, a significant research void exists with regard to strategies to enhance hazard perception under such circumstances. To address this lacuna, our study examined the potential of an intervention grounded in the knowledge-attitude-practice (KAP) framework to bolster nighttime hazard detection among drivers. We engaged a cohort of sixty drivers split randomly into an intervention group (undergoing specialized training) and a control group and employed a holistic assessment that combined eye movement analytics, physiological response monitoring, and driving performance evaluations during simulated scenarios pre- and post-intervention. The data showed that the KAP-centric intervention honed drivers' visual search techniques during nighttime driving, allowing them to confront potential threats with reduced physiological tension and ensuring more adept vehicle handling. These compelling findings support the integration of this methodology in driver training curricula and present an innovative strategy to enhance road safety during nighttime journeys.

Investigating mental workload caused by NDRTs in highly automated driving with deep learning

OBJECTIVE

This study aimed to examine the impact of non-driving-related tasks (NDRTs) on drivers in highly automated driving scenarios and sought to develop a deep learning model for classifying mental workload using electroencephalography (EEG) signals.

METHODS

The experiment involved recruiting 28 participants who engaged in simulations within a driving simulator while exposed to 4 distinct NDRTs: (1) reading, (2) listening to radio news, (3) watching videos, and (4) texting. EEG data collected during NDRTs were categorized into 3 levels of mental workload, high, medium, and low, based on the NASA Task Load Index (NASA-TLX) scores. Two deep learning methods, namely, long short-term memory (LSTM) and bidirectional long short-term memory (BLSTM), were employed to develop the classification model.

RESULTS

A series of correlation analyses revealed that the channels and frequency bands are linearly correlated with mental workload. The comparative analysis of classification results demonstrates that EEG data featuring significantly correlated frequency bands exhibit superior classification accuracy compared to the raw EEG data.

CONCLUSIONS

This research offers a reference for assessing mental workload resulting from NDRTs in the context of highly automated driving. Additionally, it delves into the development of deep learning classifiers for EEG signals with heightened accuracy.

The evaluation of eye-catching effect in freeway tunnel entrance zone based on fixation behavior characteristics

OBJECTIVE

The visual attraction of the freeway tunnel entrance zone will affect the driver's visual performance and visual workload, and then affect the driving safety. The purpose of this study is to explore the influence of different visual attraction conditions in the entrance zone of the freeway tunnel on driver's fixation behavior, and then evaluate the impact of eye-catching effect on driving safety.

METHODS

Thirty-five participants were recruited for the naturalistic driving experiment. Four different visually attractive driving scenarios (baseline, landscape-style architecture, tip slogan, and billboard) were selected. Participants' fixation behavior data were collected using an eye-tracking device. The effects of the visual attraction conditions in the freeway tunnel entrance zones were analyzed on driver fixation duration, number of fixations, fixation deviation, and pupil diameter.

RESULTS

The results demonstrated significant effects of different visual attractions in the entrance zone of the freeway tunnel on drivers' fixation behavior. Under the visual attraction of the billboard, drivers exhibited the most number of fixations. And under the visual attraction of tip slogan, drivers had the longest average fixation duration, the smallest horizontal fixation deviation, the largest vertical fixation deviation, and the highest pupil diameter and its coefficient of variation.

CONCLUSIONS

It can be concluded that different visual attractions in the freeway tunnel entrance zone have different effects on the eye-catching effect of drivers, by influencing the fixation behavior characteristics of drivers. Billboard is most effective in capturing drivers' visual attention, and tip slogan requires greater attention allocation and visual cognitive effort from drivers, resulting in the highest visual cognitive workload. This study highlights the complex relationship between visual attraction and its performance on driver fixation behavior. The insights from this study can provide valuable references for the design and optimization of the visual environment in the entrance zone of freeway tunnels.

Driver behavior while using Level 2 vehicle automation: a hybrid naturalistic study

Vehicle automation is becoming more prevalent. Understanding how drivers use this technology and its safety implications is crucial. In a 6-8 week naturalistic study, we leveraged a hybrid naturalistic driving research design to evaluate driver behavior with Level 2 vehicle automation, incorporating unique naturalistic and experimental control conditions. Our investigation covered four main areas: automation usage, system warnings, driving demand, and driver arousal, as well as secondary task engagement. While on the interstate, drivers were advised to engage Level 2 automation whenever they deemed it safe, and they complied by using it over 70% of the time. Interestingly, the frequency of system warnings increased with prolonged use, suggesting an evolving relationship between drivers and the automation features. Our data also revealed that drivers were discerning in their use of automation, opting for manual control under high driving demand conditions. Contrary to common safety concerns, our data indicated no significant rise in driver fatigue or fidgeting when using automation, compared to a control condition. Additionally, observed patterns of engagement in secondary tasks like radio listening and text messaging challenge existing assumptions about automation leading to dangerous driver distraction. Overall, our findings provide new insights into the conditions under which drivers opt to use automation and reveal a nuanced behavioral profile that emerges when automation is in use.

Impact of Partially Automated Driving Functions on Forensic Accident Reconstruction: A Simulator Study on Driver Reaction Behavior in the Event of a Malfunctioning System Behavior

Partially automated driving functions (SAE Level 2) can control a vehicle's longitudinal and lateral movements. However, taking over the driving task involves automation risks that the driver must manage. In severe accidents, the driver's ability to avoid a collision must be assessed, considering their expected reaction behavior. The primary goal of this study is to generate essential data on driver reaction behavior in case of malfunctions in partially automated driving functions for use in legal affairs. A simulator study with two scenarios involving 32 subjects was conducted for this purpose. The first scenario investigated driver reactions to system limitations during cornering. The results show that none of the subjects could avoid leaving their lane and moving into the oncoming lane and, therefore, could not control the situation safely. Due to partial automation, we could also identify a new part of the reaction time, the hands-on time, which leads to increased steering reaction times of 1.18 to 1.74 s. The second scenario examined driver responses to phantom braking caused by AEBS. We found that 25 of the 32 subjects could not override the phantom braking by pressing the accelerator pedal, although 16 subjects were informed about the system analog to the actual vehicle manuals. Overall, the study suggests that the current legal perspective on vehicle control and the expected driver reaction behavior for accident avoidance should be reconsidered.

Automation complacency on the road

Given that automation complacency, a hitherto controversial concept, is already used to blame and punish human drivers in current accident investigations and courts, it is essential to map complacency research in driving automation and determine whether current research can support its legitimate usage in these practical fields. Here, we reviewed its status quo in the domain and conducted a thematic analysis. We then discussed five fundamental challenges that might undermine its scientific legitimation: conceptual confusion exists in whether it is an individual versus systems problem; uncertainties exist in current evidence of complacency; valid measures specific to complacency are lacking; short-term laboratory experiments cannot address the long-term nature of complacency and thus their findings may lack external validity; and no effective interventions directly target complacency prevention. The Human Factors/Ergonomics community has a responsibility to minimise its usage and defend human drivers who rely on automation that is far from perfect.Practitioner summary: Human drivers are accused of complacency and overreliance on driving automation in accident investigations and courts. Our review work shows that current academic research in the driving automation domain cannot support its legitimate usage in these practical fields. Its misuse will create a new form of consumer harms.

Driver distraction and in-vehicle interventions: A driving simulator study on visual attention and driving performance

Driving simulator studies are popular means to investigate driving behaviour in a controlled environment and test safety-critical events that would otherwise not be possible in real-world driving conditions. While several factors affect driving performance, driving distraction has been emphasised as a safety-critical issue across the globe. In this context, this study explores the impact of distraction imposed by mobile phone usage, i.e., writing and reading text messages, on driver behaviour. As part of the greater i-DREAMS project, this study uses a car driving simulator experimental design in Germany to investigate driver behaviour under various conditions: (I) monitoring scenario representing normal driving conditions, (II) intervention scenario in which drivers receive fixed timing in-vehicle intervention in case of unsafe driving manoeuvres, and (III) distraction scenario in which drivers receive in-vehicle interventions based on task completion capability, where mobile phone distraction is imposed. Besides, eye-tracking glasses are used to further explore drivers' attention allocation and eye movement behaviour. This research focuses on driver response to risky traffic events (i.e., potential pedestrian collisions, and tailgating) and the impact of distraction on driving performance, by analysing a set of eye movement and driving performance measures of 58 participants. The results reveal a significant change in drivers' gaze patterns during the distraction drives with significantly higher gaze points towards the i-DREAMS intervention display (the utilised advanced driver assistance systems in this study). The overall statistical analysis of driving performance measures suggests nearly similar impacts on driver behaviour during distraction drives; a higher deviation of lateral positioning was noted irrespective of the event risk levels and lower longitudinal acceleration rates were observed for pedestrian collisions and non-critical events during distracted driving.

Analysis of mobile phone use engagement during naturalistic driving through explainable imbalanced machine learning

While driver distraction remains an issue in modernized societies, technological advancements in data collection, storage and analysis provide the means for deeper insights of this complex phenomenon. In this research, factors influencing when driver distraction through mobile phone use occurs during naturalistic driving are investigated. Naturalistic data from a 6-stage, 230-driver experiment are exploited, in which drivers installed a non-intrusive driving recording application in their devices and conducted their trips normally across a 21-month timespan, coupled with corresponding questionnaire data. The various experiment stages involved providing progressively more behavioral feedback to drivers while continuing to record them. Subsequently, supervised Machine Learning XGBoost algorithms were employed to model the contributions of naturalistic driving and questionnaire features to the decision to engage mobile phone use. Mobile phone use percentages were heavily skewed towards zero, therefore imbalanced ML with a minority-oversampling approach in a binary format was employed. To increase the explainability offered by the algorithm, SHAP values were calculated for the informative features. Results indicate that the decision of drivers to use a mobile while driving is governed by a number of complex, non-linear relationships. Total trip distance is the most significant predictor variable by a wide margin, with mean SHAP values of 0.79 towards affecting the model decisions for the probability of mobile phone use of each driver. However, other variables influence the final predictions as well, such as the number of tickets in the last three years (m.SHAP = 0.30), declared mobile phone use (m.SHAP = 0.26), the amount and variety of provided feedback (m.SHAP = 0.17) (i.e. experiment phase number) and family member numbers (m.SHAP = 0.09) decrease the probability of using a mobile phone while driving. Conversely, increases in driver experience (m.SHAP = 0.22), driver age (m.SHAP = 0.11), engine capacity (m.SHAP = 0.11) and total kilometers driven annually (m.SHAP = 0.08) increase the probability of using a mobile phone in naturalistic driving conditions. SHAP dependency plots reveal non-linear effects present in almost all variables. Fuel consumption had a particularly strong non-linear effect, as higher values of this variable lead to both higher and lower probability of drivers using a mobile phone, deviating from the safer average. Legislation, campaigns and enforcement measures can be restructured to take advantage of gains margins in terms of understanding and predicting driver distraction behavior, as explored in the present study.

Psychological Functioning, Defense Strategies and the Contribution of Perceived Family Collaboration in Adolescents Who Experienced Multiple Motor Vehicle Crashes: A Descriptive Study

Adolescents frequently experience motor vehicle collisions (MVCs). Research has mostly underlined the psychological outcomes, rather than adolescents’ general emotional–behavioral functioning and the role played by family. This study aims to explore the emotional–behavioral functioning, measured with the Youth Self Report (YSR), difficulties to identify and describe emotions, measured with the Toronto Alexithymia Scale (TAS-20), the use of defense strategies, measured with the Response Evaluation Measure for Youth (REM-71), and perceived family collaboration, measured with the Perceived Collective Family scale, in adolescents that have experienced more than three MVCs in a year. N = 150 adolescents who visited an emergency department for MVCs were assessed through self-report questionnaires. Adolescents showed difficulties to identify and describe their emotions and a massive use of defense strategies. Moreover, lower perceived family collaboration predicted adolescents’ alexithymic traits and the massive use of maladaptive defense strategies. These results may be useful in assessing and creating prevention programs for risky driving behaviors in adolescence.