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AbstractReal-time goal collaboration represents a promising approach to human-vehicle cooperative driving; however, it remains underexplored. To address this gap, we introduced an innovative human-vehicle cooperative approach and designed four interactive types with increasing autonomous levels to implement it. Additionally, we proposed seven interface design principles to design three increasing levels of transparency for the four interactive types, aiming to enhance collaboration. Experimental results demonstrate the favorable reception of the proposed cooperative approach by users. Furthermore, higher interactive autonomous levels result in reduced workload, and higher interface transparency levels lead to increased satisfaction, trust, and mutual dependence. Notably, the combination of the highest interactive autonomous level and interface transparency level, which exhibited the best performance, is recommended for practical application. This collaborative approach expands the research domain of human-vehicle cooperative driving and offers extensive potential applications across various relevant scenarios. [ABSTRACT FROM AUTHOR]

With over 1.6 million traffic deaths in 2016, automated vehicles equipped with automated driving systems (ADSs) have the potential to increase traffic safety by assuming human driving tasks within the operational design domain (ODD). However, safety validation is challenging due to the open-context problem. Current strategies, such as pure driving and requirement-based testing, are insufficient. Scenario-based testing offers a solution but necessitates appropriate scenario selection, testing methods, and evaluation criteria. This paper builds upon a method to calculate the covered ODD using tested scenarios generated from logical scenarios, considering parameter discretisation uncertainty. Acceptance criteria for the safety argumentation are proposed based on parameter space coverage and variance introduced via discretisation, thus contributing to quantifying the residual risks of safety validation. The approach is demonstrated through two logical scenarios with probability density functions of the parameters generated using a trajectory dataset. These criteria can serve as risk acceptance criteria, providing comparability and explainable results. By developing a robust scenario-based testing approach, ADS safety can be validated, leading to increased traffic safety and reduced fatalities. Since ADSs incorporate AI models, this proposed validation strategy can be extended to AI systems across multiple domains for the respective assurance argument required for deployment. [ABSTRACT FROM AUTHOR]

Automated driving systems (ADSs) are complex entities comprising numerous components, and traditional testing methods often struggle to ensure their safety, primarily due to the diversity driving environments. Interestingly, deep neural networks (DNNs) have proven effective for object detection in these settings. The safety of object detection in ADSs depends on the position of the detected objects and the specifications that guide the system’s response to them. Consequently, testing the object-detection process in ADSs must be grounded in these specifications. However, current specifications are informal regarding object locations and inadequate for object-detection testing. To address this issue, this article first introduces the bounding box specification language (BBSL), a framework capable of mathematically articulating the specifications for object and event detection and responses. Subsequently, we propose a specification-based testing approach for the object-detection process in ADS using BBSL. Remarkably, BBSL can formally delineate the positions of objects within the driving environment. Furthermore, our proposed approach can identify safety-critical defects that conventional tests, which focus solely on performance evaluation, might overlook. Furthermore, we propose two sets of test criteria. The first set reflects the diversity of object positions and sizes within an image, while the second set includes coverage metrics that determine whether the test cases cover all conditions outlined by the BBSL specifications. Overall, our contributions facilitate the implementation of specification-based testing for object-detection systems using DNNs, a challenge previously considered formidable.

Abstract The current paper was performed within the HADRIAN project and focuses on exploring the effects of innovative Human–Machine Interface (HMI) prototypes on safety, driving performance, and driver perceptions. Employing driving simulator experiments and questionnaires, this study investigates whether HADRIAN innovative HMI enhances safety and receives positive evaluations from drivers. Specifically, the research centers on a driving simulator experiment that evaluates novel HMI prototypes designed to improve automated driving at SAE Levels 2 or 3. To facilitate HMI assessment, a tailored safety and impact assessment methodology was developed using unique Key Performance Indicators (KPIs). To benchmark and generate a total score for the HADRIAN HMI, data envelopment analysis was deployed based on the aforementioned KPIs. The findings shed light on the influence of HADRIAN HMI innovations on safety and perceived impact when compared to a baseline “state-of-the-art” HMI. Subsequently, a comprehensive discussion unfolds, highlighting the key KPIs that contributed significantly to the safety and perceived impact scores. This method and its outcomes can serve as a valuable resource for other HMI stakeholders, enabling them to employ similar human-centered assessment methodologies to assess the safety and perceived impact of potential HMI configurations.

Abstract Multispectral pedestrian detection with visible light and infrared images is robust to changes in lighting conditions and therefore is of great importance to numerous applications that require all‐day environmental perception. This paper proposes a novel method named FCE‐RCNN, which integrates saliency detection as a sub‐task and utilizes global information for enhanced feature representation. The approach enhances thermal inputs by incorporating gradients at the raw‐data level before feature extraction. Utilizing a dual‐stream backbone, a global semantic information extraction module is introduced that combines pooling with horizontal–vertical attention mechanisms, capturing high‐quality global semantic information for lower‐level feature enrichment and guidance. Additionally, the pedestrian locality enhancement module is designed to enhance spatial locality information of pedestrians through saliency detection. Furthermore, to alleviate the challenges posed by positional shifts between cross‐spectral features, deformable convolution is innovatively employed. Experimental results on the KAIST dataset demonstrate that FCE‐RCNN significantly improves nighttime detection, achieving a log‐average miss rate of 6.92%, outperforming the new method ICAFusion by 0.93%. These results underscore the effectiveness of FCE‐RCNN, and the method also maintains competitive inference speed, making it suitable for real‐time applications.

This study provides a systematic analysis of the resource-consuming training of deep reinforcement-learning (DRL) agents for simulated low-speed automated driving (AD). In Unity, this study established two case studies: garage parking and navigating an obstacle-dense area. Our analysis involves training a path-planning agent with real-time-only sensor information. This study addresses research questions insufficiently covered in the literature, exploring curriculum learning (CL), agent generalization (knowledge transfer), computation distribution (CPU vs. GPU), and mapless navigation. CL proved necessary for the garage scenario and beneficial for obstacle avoidance. It involved adjustments at different stages, including terminal conditions, environment complexity, and reward function hyperparameters, guided by their evolution in multiple training attempts. Fine-tuning the simulation tick and decision period parameters was crucial for effective training. The abstraction of high-level concepts (e.g., obstacle avoidance) necessitates training the agent in sufficiently complex environments in terms of the number of obstacles. While blogs and forums discuss training machine learning models in Unity, a lack of scientific articles on DRL agents for AD persists. However, since agent development requires considerable training time and difficult procedures, there is a growing need to support such research through scientific means. In addition to our findings, we contribute to the R&D community by providing our environment with open sources.

The interface of L3 automated driving systems needs to take both automated and manual driving into consideration. This calls for a comprehension of drivers' differed risk perception under manual and automated driving conditions, thus supporting description of contextual information and appropriate risk warnings. Existing studies have reported drivers' impaired ability during automated driving, however, a quantified measurement is still lacking. This study tried to measure the difference in drivers' ability to perceive risks during automated and manual driving. Specifically, a simulated driving experiment in car-following scenarios was conducted to collect drivers' perceived risk under multiple manual and automated driving conditions, including varied motion directions, speed, and distance among vehicles. Then, the influences of driving mode, motion directions, speed, and distance on drivers' risk perceptions were described using a linear mixed model. The result demonstrated a complicated interaction effect. Automated driving impaired drivers' risk perception, and this effect was less severe in highly risky events. In both manual and automated driving, drivers were less sensitive to risk only when risky events happened backwards. These results indicated drivers' varied ability under multiple conditions, and supported warning design and interface refinement under automated and manual driving conditions. [ABSTRACT FROM AUTHOR]

The current paper was performed within the HADRIAN project and focuses on exploring the effects of innovative Human–Machine Interface (HMI) prototypes on safety, driving performance, and driver perceptions. Employing driving simulator experiments and questionnaires, this study investigates whether HADRIAN innovative HMI enhances safety and receives positive evaluations from drivers. Specifically, the research centers on a driving simulator experiment that evaluates novel HMI prototypes designed to improve automated driving at SAE Levels 2 or 3. To facilitate HMI assessment, a tailored safety and impact assessment methodology was developed using unique Key Performance Indicators (KPIs). To benchmark and generate a total score for the HADRIAN HMI, data envelopment analysis was deployed based on the aforementioned KPIs. The findings shed light on the influence of HADRIAN HMI innovations on safety and perceived impact when compared to a baseline "state-of-the-art" HMI. Subsequently, a comprehensive discussion unfolds, highlighting the key KPIs that contributed significantly to the safety and perceived impact scores. This method and its outcomes can serve as a valuable resource for other HMI stakeholders, enabling them to employ similar human-centered assessment methodologies to assess the safety and perceived impact of potential HMI configurations. [ABSTRACT FROM AUTHOR]

Objective: This study examined the impact of monitoring instructions when using an automated driving system (ADS) and road obstructions on post take-over performance in near-miss scenarios. Background: Past research indicates partial ADS reduces the driver's situation awareness and degrades post take-over performance. Connected vehicle technology may alert drivers to impending hazards in time to safely avoid near-miss events. Method: Forty-eight licensed drivers using ADS were randomly assigned to either the active driving or passive driving condition. Participants navigated eight scenarios with or without a visual obstruction in a distributed driving simulator. The experimenter drove the other simulated vehicle to manually cause near-miss events. Participants' mean longitudinal velocity, standard deviation of longitudinal velocity, and mean longitudinal acceleration were measured. Results: Participants in passive ADS group showed greater, and more variable, deceleration rates than those in the active ADS group. Despite a reliable audiovisual warning, participants failed to slow down in the red-light running scenario when the conflict vehicle was occluded. Participant's trust in the automated driving system did not vary between the beginning and end of the experiment. Conclusion: Drivers interacting with ADS in a passive manner may continue to show increased and more variable deceleration rates in near-miss scenarios even with reliable connected vehicle technology. Future research may focus on interactive effects of automated and connected driving technologies on drivers' ability to anticipate and safely navigate near-miss scenarios. Application: Designers of automated and connected vehicle technologies may consider different timing and types of cues to inform the drivers of imminent hazard in high-risk scenarios for near-miss events. [ABSTRACT FROM AUTHOR]

Automated driving systems are deployed on public roads with little empirical support for the dominant justifications of enhanced safety and enhanced productivity. Furthermore, development of automated driving systems has been piecemeal rather than systematic while research on driver-automation interaction has relied on individual analysis of accidents and on observational studies of driving behavior in a simulator or on the road. In this paper, we apply Work Domain Analysis to develop a more systematic and comprehensive model of automated driving. We use a strategy of layering the driving automation onto the resulting Abstraction-Decomposition Space for manual driving to mimic the existing design strategy of introducing automation to take over driving functions previously the responsibility of the human driver. Our analysis shows that automation does not unequivocally supports dominant driving values. Furthermore, our analysis revealed subtle interdependencies between human and technological functions. We conclude that an Abstraction Decomposition Space offers a systematic view of driver-automation interaction that can suggest new insights for automation design. [ABSTRACT FROM AUTHOR]

To enable successful automated driving, precise behavior prediction of surrounding vehicles is indispensable in urban traffic scenarios. Furthermore, given that a vehicle's behavior is influenced by the movements of other road users, it becomes crucial to estimate their intentions to anticipate precise future motion. However, the elevated complexity resulting from interdependencies among traffic participants and the uncertainty arising from the object recognition errors present additional challenges. Despite extensive research on inferring intentions, many studies have concentrated on estimating intentions from interactions, resulting in a lack of practicality in urban traffic environments due to low computational efficiency and low robustness against recognition failure of strongly interacting road users. In this paper, we introduce a practical stochastic model for intention estimation and trajectory prediction of surrounding vehicles in automated driving under urban traffic environments. The trajectory is forecasted based on hierarchically computed and probabilistically estimated intentions, which represent an interpretation of vehicle behavior, utilizing only the kinematic state of the focal vehicle and HD maps to ensure real-time performance and enhance robustness. The evaluated results demonstrate that the proposed model surpasses straightforward methods in terms of accuracy while maintaining computational efficiency and exhibits robustness against the recognition failure of traffic participants which strongly influence the focal vehicle. [ABSTRACT FROM AUTHOR]

Multispectral pedestrian detection with visible light and infrared images is robust to changes in lighting conditions and therefore is of great importance to numerous applications that require all‐day environmental perception. This paper proposes a novel method named FCE‐RCNN, which integrates saliency detection as a sub‐task and utilizes global information for enhanced feature representation. The approach enhances thermal inputs by incorporating gradients at the raw‐data level before feature extraction. Utilizing a dual‐stream backbone, a global semantic information extraction module is introduced that combines pooling with horizontal–vertical attention mechanisms, capturing high‐quality global semantic information for lower‐level feature enrichment and guidance. Additionally, the pedestrian locality enhancement module is designed to enhance spatial locality information of pedestrians through saliency detection. Furthermore, to alleviate the challenges posed by positional shifts between cross‐spectral features, deformable convolution is innovatively employed. Experimental results on the KAIST dataset demonstrate that FCE‐RCNN significantly improves nighttime detection, achieving a log‐average miss rate of 6.92%, outperforming the new method ICAFusion by 0.93%. These results underscore the effectiveness of FCE‐RCNN, and the method also maintains competitive inference speed, making it suitable for real‐time applications. [ABSTRACT FROM AUTHOR]

Human-machine co-driving presents a significant hurdle in automated driving system. The takeover process in automated driving system involves complex human factors, failure to takeover the vehicle and control driving behavior during the takeover process may lead to severe traffic safety hazards. An augmented reality head-up display (AR-HUD) takeover assistance information can provide real-time assistance information to the driving environment, enhancing drivers' situation awareness (SA) and takeover decisions in highly automated driving system. This study investigated the impact of different AR-HUD types of takeover assistance information display. Three AR-HUD types, corresponding to the three pre-takeover behavioral processes (perception, understanding, and prediction), were evaluated: PSR (assistance in perceiving the source of risk), AS (assistance in analyzing situations), and MD (assistance in making decisions). The baseline (without assistance information) was used as the control group. In a driving simulation experiment using 360° panoramic video, seventy-nine participants performed SA assessment and visual tracking tasks. Questionnaire and eye-tracking data indicated that the type of AR-HUD displayed positively influenced drivers' SA and takeover decisions, with AS being the most effective in enhancing SA and improving takeover performance. Additionally, this study compared the differences between the three types of AR-HUD and the baseline under two takeover request lead times (TORlt) of 5 seconds and 7 seconds. It was found that drivers' SA was lower when TORlt was shorter (with the corresponding AR-HUD display also being shorter). This study provides insight concerning the impact of various types of AR-HUD takeover assistance information display and TORlt on driving safety. The findings support the further optimization of AR-HUD takeover assistance information design. [ABSTRACT FROM AUTHOR]

The upcoming introduction of automated vehicles is expected to reduce the incidence of traffic accidents caused by human error. However, conditionally automated driving faces a significant challenge in that the driver must promptly take control when a request to intervene (RtI) is issued. Given this context, many studies have investigated the characteristics of the time it takes drivers to take control following an RtI and the factors influencing this time. It is also known that the driver's take-over response following an RtI is similar to the driver's braking response to the brake light of a lead vehicle or the yellow light of a traffic signal. Hence, findings on driver braking responses can provide valuable insights for take-over research. For example, gender was found to differentially affect perception time (PT) and movement time (MT), which are both subcomponents of the brake-response time in manual driving. Positive correlations were observed between these two times. These characteristics could be expected for PT and MT in take-over time (TOT) due to the similarities between braking and take-over responses; however, to the best of our knowledge, no study has yet examined these characteristics. Therefore, the present study was conducted to experimentally determine whether driver gender differentially affects the PT and MT, which are both subcomponents of TOT. Additionally, the correlations between these components were examined. This study provides novel insights into PT and MT during non-critical takeovers, facilitating a better understanding of TOT. Specifically, among drivers up to middle age, who were the target demographic group of this study, females tended to have a shorter PT, whereas males tended to have a shorter MT. This indicates that gender-specific approaches may be effective at reducing TOT, as gender is associated with differences in PT and MT. In addition, positive correlations were observed between PT, MT, and TOT. The correlation between PT and TOT indicates PT as an effective predictor of TOT. TOT data were collected in non-critical take-over scenarios, which are typical for conditionally automated vehicles. A total of 311 trials were performed using a driving simulator with 15 male and 15 female participants, resulting in 300 take-over data points. TOT was divided into PT and MT using video coding performed by two coders. Gender in this study was defined as being a biological male or female, and was obtained following the participants' permission. • Take-over time (TOT) was decomposed into perception time (PT) and movement time (MT). • Females tended to have a shorter PT, while males tended to have a shorter MT. • This tendency indicates that effective training to reduce TOT may differ by gender. • Positive correlations were found between PT, MT, and TOT. • The correlation between PT and TOT indicates that PT may be a predictor of TOT. [ABSTRACT FROM AUTHOR]

• Real-time trust estimation model is proposed, which is dynamic and quantitative, considering the evolution pattern of driver's trust and the perceived risk; • Mathematical modeling and machine learning methods are combined; • A trust-based reminder strategy that aims to enhance the safety of human–machine co-driving is designed; • Driver-in-loop experiment validates the effectiveness in enhancing the safety, maintaining driver's trust and reducing trust biases in human–machine co-driving. The development of automated vehicles (AVs) will remain in the stage of human–machine co-driving for a long time. Trust is considered as an effective foundation of the interaction between the driver and the automated driving system (ADS). Driver's trust miscalibration, represented by under-trust and over-trust, is considered to be the potential cause of disuse and misuse of ADS, or even serious accidents. The estimation and calibration of trust are crucial to improve the safety of the driving process. This paper mainly consists of the following two aspects. Firstly, a dynamic and quantitative trust estimation model is established. A framework for trust estimation is constructed. Driver's perceived risk and behavior features were monitored and a Kalman filter was used to dynamically and quantitatively estimate the driver's trust. We conducted a driver-in-the-loop experiment and generated model parameters through a data-driven approach. The results demonstrated that the model exhibited precision in trust estimation, with the highest accuracy reaching 74.1%. Secondly, a reminder strategy to calibrate the over-trust of the driver is proposed based on the model from the first part. A scenario with four risky events was designed and the ADS would provide voice reminders to the driver when over-trust was detected. The results demonstrated that the reminder strategy proved to be beneficial for safety enhancement and moderate trust maintenance during the driving process. When the driver is over-trusting, the accident rates of the reminder group and the non-reminder group were 60.6% and 13.0%, respectively. Our contribution in this paper can be concluded by four points: (1) A real-time trust estimation model is proposed, which is dynamic and quantitative, considering the evolution pattern of driver's trust and the perceived risk; (2) Mathematical modeling and machine learning methods are combined; (3) A trust-based reminder strategy that aims to enhance the safety of human–machine co-driving is designed; (4) Driver-in-loop experiment validates the effectiveness in enhancing the safety, maintaining driver's trust and reducing trust biases in human–machine co-driving. [ABSTRACT FROM AUTHOR]

As an alternative to the conventional control allocation, as described by the SAE, cooperative driving concepts have emerged in recent research. These concepts aim towards making the human driver and automated vehicle work together in a team. For effective collaboration, it is critical that the driver be able to predict the actions of the automation. In a simulator study, we investigated the effects of communicating the automation's decision and level of confidence to the human driver in order to increase predictability in a cooperative driving scenario. We found that participants tended to take longer to make a choice when a decision and level of confidence were displayed. Additionally, displaying the automation's decision significantly increased the participants' decision time and improved the correctness of their decisions. In addition, all tested HMIs received positive evaluations for trust in automation and usability, with the Baseline HMI receiving significantly higher scores. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate the impact of automated vehicle (AV) interaction mode on drivers' trust and preferred driving styles in response to pedestrian- and traffic-related road events. Background: The rising popularity of AVs highlights the need for a deeper understanding of the factors that influence trust in AV. Trust is a crucial element, particularly because current AVs are only partially automated and may require manual takeover; miscalibrated trust could have an adverse effect on safe driver-vehicle interaction. However, before attempting to calibrate trust, it is vital to comprehend the factors that contribute to trust in automation. Methods: Thirty-six individuals participated in the experiment. Driving scenarios incorporated adaptive SAE Level 2 AV algorithms, driven by participants' event-based trust in AVs and preferences for AV driving styles. The study measured participants' trust, preferences, and the number of takeover behaviors. Results: Higher levels of trust and preference for more aggressive AV driving styles were found in response to pedestrian-related events compared to traffic-related events. Furthermore, drivers preferred the trust-based adaptive mode and had fewer takeover behaviors than the preference-based adaptive and fixed modes. Lastly, participants with higher trust in AVs favored more aggressive driving styles and made fewer takeover attempts. Conclusion: Adaptive AV interaction modes that depend on real-time event-based trust and event types may represent a promising approach to human-automation interaction in vehicles. Application: Findings from this study can support future driver- and situation-aware AVs that can adapt their behavior for improved driver-vehicle interaction. [ABSTRACT FROM AUTHOR]

Because driving automation is quickly increasing, humans' relation to automated driving evolves accordingly. Trust in automated driving has been shown to strongly correlate with drivers' acceptance of such systems. There is still much to be learned about which factors determine trust before any interaction with Highly Automated Driving (HAD) systems. Theoretical trust described in automation models proposes several factors to explain how trust builds before and during interactions with automation. Using 844 answers collected through an online survey, this article aims to propose a new scale specifically designed to evaluate initial trust in automated driving. Moreover, we also measure the other factors linked to drivers' initial level of trust in HAD by operationalizing the trust components proposed by Hoff and Bashir's theoretical trust model. To better describe what determines trust in HAD, a linear model based on the collected data is proposed. The model not only weights the factors determining trust but is also able to predict the level of trust considering these factors. [ABSTRACT FROM AUTHOR]

New driver assistance systems play an important role to rise safety and comfort in todays´ traffic. Those systems should be developed with the needs of the user in mind and tested for the users´ requirements. In this, user acceptance is a central variable of interest – both in scientific and in practical applications of user-centered research on driver assistance systems. In some cases, applied research settings need simplified measurements in order to be efficiently applicable in the driving situations. In the present paper, we explored the applicability and validity of a single-item acceptance measurement (SIAM) for practical study settings covering the attitude towards using new driver assistance systems. To provide a theoretical framing, we tested the one-item measure against the widely used Technology Acceptance Model (TAM) and the van der Laan acceptance scale (VDL) in a driving simulator study. Participants experienced four different complex driving scenarios using a driver assistance system. Acceptance was measured repeatedly throughout the drive. The results supported construct validity for the SIAM, correlating with the VDL. The SIAM further predicted the intention to use the system. Being carefully aware of the psychometric drawbacks of short scales and acknowledging the importance of multi-item scales, the SIAM is promising for efficiently approaching the acceptance of driver assistance systems in applied settings. [ABSTRACT FROM AUTHOR]

The acceptance of automated vehicles and advanced vehicle technologies by users is subject to different human factors variables. Personality, technology adoption, and prior previous knowledge about the systems have been significant determinants of people’s attitudes toward new technologies across different settings. The present study examined the effects of technology adoption, knowledge of vehicle automation, and personality on the preferred level of vehicle automation in Türkiye and Sweden. The study was conducted with 297 drivers from Türkiye (age: M = 22.47, SD = 2.83) and 332 drivers from Sweden (age: M = 30.06, SD = 10.48). Participants completed a questionnaire regarding technology adoption, knowledge and preference of vehicle automation, and the Basic Personality Traits Inventory (BPTI). The findings indicated that high technology adoption was associated with preferring higher levels of automation. Furthermore, drivers from Türkiye, in comparison to drivers from Sweden, and drivers with previous knowledge of high or full automation, compared to those who have not heard of these systems in the two countries, expressed a preference toward higher levels of automation. High extraversion and openness to change were associated with high technology adoption, leading to preferring vehicles with higher levels of automation. Overall, the results indicated that drivers’ knowledge of automated vehicles and general traits, such as personality and technology adoption, play a role in vehicle preference. The study analyzed the factors that affect user acceptance of automated vehicles and offered insights into their interrelationships across two countries with differing levels of road safety.

Objective: This study developed a fixation-related electroencephalography band power (FRBP) approach for situation awareness (SA) assessment in automated driving. Background: Maintaining good SA in Level 3 automated vehicles is crucial to drivers' takeover performance when the automated system fails. A multimodal fusion approach that enables the analysis of the visual behavioral and cognitive processes of SA can facilitate real-time assessment of SA in future driver state monitoring systems. Method: Thirty participants performed three simulated automated driving tasks. After each task, the Situation Awareness Global Assessment Technique (SAGAT) was deployed to capture their SA about key elements that could affect their takeover task performance. Participants eye movements and brain activities were recorded. Data on their brain activity after each eye fixation on the key elements were extracted and labeled according to the correctness of the SAGAT. Mixed-effects models were used to identify brain regions that were indicative of SA, and machine learning models for SA assessment were developed based on the identified brain regions. Results: Participants' alpha and theta oscillation at frontal and temporal areas are indicative of SA. In addition, the FRBP technique can be used to predict drivers' SA with an accuracy of 88% using a neural network model. Conclusion: The FRBP technique, which incorporates eye movements and brain activities, can provide more comprehensive evaluation of SA. Findings highlight the potential of utilizing FRBP to monitor drivers' SA in real-time. Application: The proposed framework can be expanded and applied to driver state monitoring systems to measure human SA in real-world driving. [ABSTRACT FROM AUTHOR]

• We investigated a motion sickness mitigation method during a real car drive. • Vibrotactile cues announcing upcoming driving manoeuvres mitigate motion sickness. • Auditory cues are less effective than vibrotactile cues in mitigating sickness. • Passengers express greater preference for vibrotactile cues. Car passengers experience much more car sickness than car drivers. We assume that this is because drivers can better anticipate the car's motions. Does helping passengers to anticipate the car's motions then mitigate car sickness? Indeed, laboratory studies have shown that anticipatory cues which announce one-dimensional motions of a linear sled mitigate sickness to a small extent. Does this mitigation generalize to real car driving? We tested this in a car ride on a test track along a trajectory involving lane changes, accelerations, and decelerations. We show that vibrotactile cues mitigated car sickness in passengers. Auditory cues were less effective. The mitigating effect of the vibrotactile cue was considerable: a 40% decrease in car sickness symptoms, a larger effect than we found in the laboratory. Automated vehicles can predict their own motion very well. They could thus provide vibrotactile cues to mitigate car sickness in their passengers. [ABSTRACT FROM AUTHOR]

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This article focuses on how car drivers domesticate technologies of automation and the way this might inform our understanding of potential shifts to a more automated mobility system. The current literature on automated mobility has mainly addressed drivers' roles in terms of their attitudes towards--and acceptance of--an anticipated shift to high-level driving automation. In this article, however, we take a step back from expectations around automated mobility to explore the domestication of driving assistance technologies and systems already in use. The analysis is built on qualitative interviews with drivers of private cars in Norway. Based on our findings, we develop a typology of user-technology characterisations highlighting three themes of the drivers' use (comfort, safety, and novelty) as well as two modes of engagements (modulation and non-use). Our analysis suggests that automation is likely to be an incremental and gradual process and that its eventual application depends on the specificities of the practices that it seeks to disrupt. Moreover, we argue that the governance of automated mobility needs to be attentive to the dynamic and unpredictable roles technology will have in processes of socio-technical change. In this context, we highlight the key roles of users in shaping processes of appropriation of both new technologies and broader innovations and argue that knowledge about technology domestication provides important insights to changes towards automation in our current mobility systems. [ABSTRACT FROM AUTHOR]

Drivers of Level 3 automated vehicles are relieved from the driving task in specific circumstances but are required to take over control once the takeover request is prompted. Previous studies have investigated drivers' takeover performance in non-critical car-following. However, little is known about drivers' takeover behaviors in critical car-following, especially in low-visibility weather, which remarkably increases the risk of car-following. A driving simulator experiment with a 2 × 3 × 3 factor within-group design was conducted. The design matrix contained two weather conditions (clear and foggy), three car-following time headways (2 s, 3 s, 4 s) and three deceleration rates of the lead vehicle (LV) (0, 2 m/s2, 4 m/s2). A total of 30 participants completed the experiment. The results showed that in critical car-following situations, drivers faced greater challenges in negotiating with adjacent vehicles rather than the LV itself. Urgency and visibility did not significantly impact the likelihood of a crash with the LV due to drivers' adoption of stronger braking. However, decreased visibility and higher LV's deceleration increased the crash rate when drivers attempted lane changes. As urgency increased, drivers tended to change lanes earlier, leading to higher lane-changing risks and compromised lateral stability. This study can provide some insights for the car-following strategies of automated driving vehicles and the design of dedicated takeover schemes in various transportation environments. [ABSTRACT FROM AUTHOR]

• This is the first study to compare controlled non-driving related tasks in terms of car sickness incidence. • Tasks with visual input induce more car sickness than internal vision of the car. • Tasks with visual dynamic input are most effective in inducing car sickness. • However, only internal vision of the car still produces mild to moderate symptoms. With the development of highly automated driving functions, drivers will no longer be in full charge of the driving task and can instead engage in a variety of non-driving related tasks (NDRTs), such as reading or watching a movie. However, engaging in these tasks increases the risk of experiencing motion sickness in a car. So far, most studies have compared everyday tasks such as reading and watching movies regarding their impact on car sickness. In this on-road driving study, a more theoretical approach was taken and controlled tasks were chosen to compare certain task characteristics regarding their impact on car sickness. In a within subject design, N = 20 moderately to severely susceptible participants completed three experimental drives on separate days, each with one task. To induce car sickness, a standardized driving profile including highly dynamic manoeuvres was driven on open roads. Three tasks with different types of visual input were selected: an auditory n-back task, a static visual n-back task, and a dynamic visual task. Participants were instructed to look down at a tablet throughout the drive and not to look up through the windscreen in all conditions. Driving dynamics, task performance and mental workload of the tasks were used as control variables. The effect of the tasks on the occurrence of car sickness was evaluated using subjective misery scale ratings. On average a medium to high level of car sickness could be induced over all trials. The extent of car sickness differed significantly between task conditions. Both visual tasks produced more car sickness than the auditory task, with the visual dynamic task leading to the most severe symptoms. Visual input in NDRTs, particularly moving images, seem to play a crucial role for the occurrence of car sickness. Possible underlying mechanisms are discussed and methodological implications for the use of NDRTs in realistic driving studies to elicit car sickness are derived. [ABSTRACT FROM AUTHOR]

The decline in reaction times, memory, and motor abilities in elderly drivers during takeover situations is welldocumented. Vibration cues have been identified as promising reminders for takeovers, given their non-interfering nature with driving task and their ability to effectively occupy the perceptual channel. However, the positioning and order of vibration cues may influence drivers' takeover capabilities. In different risk scenarios, the differences of the sequence types of vibration cues on the take-over behaviors and subjective feelings of drivers of different ages were compared, so as to help elderly drivers better complete the take-over. Twelve drivers, comprising six young drivers and six elderly drivers, participated in the study and viewed accident videos depicting different risk scenarios. Prior to the accidents, participants were given takeover prompts using various vibration cue sequences. Subjective questionnaires were then employed to compare the differences in takeover urgency and risk perception between the age groups in response to different vibration cue types. The results indicate that drivers demonstrate higher takeover willingness in low-risk scenarios (F = 5. 872, P = 0. 018) and require longer lead times to detect risks in high-risk scenarios. Furthermore, elderly drivers exhibit significantly lower trust in automated vehicles compared to young drivers ( F = 30. 912, P < 0. 001) and have a lower level of risk awareness. For elderly drivers, the vibration cue sequence consisting of multiple consecutive points proves most effective in indicating the level of danger and takeover urgency. In contrast, for young drivers, both the continuous vibration cue sequence and the consecutive vibration cue sequence with multiple points are effective in indicating front-end danger, with the continuous vibration cue sequence performing best in prompting takeover urgency. [ABSTRACT FROM AUTHOR]

• Alcohol impairs perception, cognition, and action in manual driving, increasing accident risk. • Higher blood alcohol concentrations (BACs) result in more significant negative effects on driving. • More experienced drivers and drivers with more frequent alcohol consumption may be less impacted by the alcohol. • Limited studies on automated vehicles show similar impairments due to alcohol as in manual driving. • Methodological biases in reviewed studies may impact generalizability of findings. • Recommendations include in-car systems and assistive driving tech to improve road safety. Introduction : Almost a third of car accidents involve driving after alcohol consumption. Autonomous vehicles (AVs) may offer accident-prevention benefits, but at current automation levels, drivers must still perform manual driving tasks when automated systems fail. Therefore, understanding how alcohol affects driving in both manual and automated contexts offers insight into the role of future vehicle design in mediating crash risks for alcohol-impaired driving. Method: This study conducted a systematic review on alcohol effects on manual and automated (takeover) driving performance. Fifty-three articles from eight databases were analyzed, with findings structured based on the information processing model, which can be extended to the AV takeover model. Results: The literature indicates that different Blood Alcohol Concentration (BAC) levels affect driving skills essential for traffic safety at various information processing stages, such as delayed reacting time, impaired cognitive abilities, and hindered execution of driving tasks. Additionally, the driver's driving experience, drinking habits, and external driving environment play important roles in influencing driving performance. Conclusions : Future work is needed to examine the effects of alcohol on driving performance, particularly in AVs and takeover situations, and to develop driver monitoring systems. Practical applications: Findings from this review can inform future experiments, AV technology design, and the development of driver state monitoring systems. [ABSTRACT FROM AUTHOR]