Driving Simulator Study Research Articles

Driving Abilities and Wearing-Off in Parkinson’s Disease: A Driving Simulation Study

Background/Objectives: Driving abilities require the synchronized activity of cerebral networks associated with sensorimotor integration, motricity, and executive functions. Drivers with Parkinson’s disease (DwP) have impaired driving ability, but little is known about the impact of “wearing-off” and therapies in addition to L-DOPA on driving capacities. This study aimed to (i) compare driving performance between DwP during different motor states and healthy controls and (ii) assess the impact of add-on therapies on driving abilities. Methods: DwP (n = 26) were enrolled as individuals experiencing wearing-off symptoms and treated (within 6 months before the enrollment) with add-on therapies to L-DOPA, including MAO inhibitors for DwP-A (n = 12) or opicapone for DwP-B (n = 14). Age- and sex-matched controls (CON, n = 12) were also enrolled. DwP received two driving assessments in a driving simulator during their “best-on” time and during their wearing-off time on different days. An anamnestic driving questionnaire was collected with the assistance of partners. A Virtual Driving Rating Scale (VDRS) was calculated, as well as learning curves (LCs) for driving items calculated in minutes. Results: DwP reported worse driving performance than CON at the driving questionnaire. In line with this, DwP showed worse VDRS (p < 0.01) and LC (p = 0.021) than CON. Lower VDRS was associated with wearing-off (p < 0.01), but DwP-B had better driving performance while in their “best-on” time (p = 0.037) and more items improving with LCs (7 vs. 3) than DwP-A. Conclusions: DwP demonstrated impaired driving compared to controls. Wearing-off symptoms can also affect driving ability, but therapies (opicapone more so than MAO inhibitors) may play a role in preserving specific driving skills, possibly through maintaining learning abilities.

Supporting Driver Attention Toward Potential Hazards During Takeover: A Preliminary Result

This study investigates the impact of a supportive system on takeover transitions in conditionally automated driving (SAE Level 3). The supportive system is designed to direct drivers’ attention toward potential hazards in the environment when a takeover request occurs. The study comprises two components: (a) identifying various types of potential hazards using naturalistic driving data, and (b) conducting a driving simulator study to develop and assess a gaze guidance system based on the N-SEEV model of visual attention. Results indicate that drivers using a highly salient attention guidance system were less likely to collide with a secondary hazard during takeover transitions. This suggests that gaze guidance support is an effective approach for assisting drivers during takeover transitions.

Validating risk behavior in driving simulation using naturalistic driving data

The absence of physical accident risk in driving simulation, which allows for safely studying critical driving situations, also reduces the driver's risk perception, which may result in unrealistic driver behavior. Validation studies in this context are rare for ethical reasons, making it difficult to assess the extent of this issue at present. The present study addresses this gap by utilizing naturalistic driving data. Four critical cut-in situations on German highways were extracted from naturalistic data and replicated in a driving simulator study with N = 58 participants. Both in-situ self-ratings on subjective criticality and post-hoc video-based ratings (from the driver and objective observers), as well as presence ratings, were collected to supplement driver behavior. Although driver reactions in the simulator and the field were not equivalent in absolute terms, drivers in both the simulation and the real world exhibited accident-avoidance behavior through braking reactions, indicating relative validity. No clear mediating role of the sense of presence towards a more careful driver behavior was found. This work shows that drivers exhibit accident-avoiding behavior in the simulator and tend to react to hazards in the simulator similarly as they would in a real situation, while absolute numerical values should only be interpreted with caution.

Mixing transcutaneous vagal nerve stimulation and galvanic cutaneous stimulation to decrease simulator adaptation syndrome.

Simulator Adaptation Syndrome arises from a perceptual discordance between expected and actual motion, giving rise to symptoms such as nausea and disorientation. This research focused on determining the benefit of Transcutaneous Vagal Nerve Stimulation (tVNS) and Galvanic Cutaneous Stimulation (GCS), where both were applied in conjunction, as compared to their administration in isolation, to decrease Simulator Adaptation Syndrome (SAS). A driving simulation study was proposed where SAS, body balance, and driving performance were measured. These measurements were taken during seven different stimulation scenarios with a baseline condition without stimulation compared against tVNS and GCS conditions. The main result showed that the combination of tVNS and GCS reduced SAS and improved body balance and driving performance more successfully than their administration in isolation. Similar neuromodulation in the temporoparietal junction is proposed to mitigate SAS for GCS and tVNS (although additional explanations are discussed). Applying both techniques simultaneously is encouraged to decrease SAS in future interventions.

Impact of Median Separation Facilities on Drivers’ Shy-Away Effects: Why Are Median Separation Facilities Widely Used in China?

In recent years, China has begun introducing readily installable median separation facilities into its urban road network. Although some studies have recognized the benefits of using median separation, few have investigated the effects of different types of facilities on driver behavior. This driving simulator study aims to determine whether different types of medians on urban arterial roads in China affect driving behavior, and to provide new insights for designing more effective and safer forms of median separation. A driving simulator study was conducted, with 50 participants. The scenario exactly reproduced an existing six-lane urban arterial road in China, and four different median separation types were tested: (1) double yellow line; (2) raised pavement markers; (3) reboundable traffic cylinders; (4) traffic separator railing. Their effects were statistically analyzed using data on driver eye movements, trajectories, and speeds collected from simulation tests. The results showed that the median separation facilities significantly improved drivers’ access to road information, corrected drivers’ attention, and suppressed driving speeds. Although median separation facilities can be an effective solution to the current problem of chaotic vehicular traffic on China’s urban roads, their setup still needs to be optimized. This study demonstrates the calculation method for optimal median facility setup. The resulting 1.21-m-high median facility, paired with a 1.17-m-wide median strip and a certain lane-width compression, more effectively addresses driver factors, the driving task, and the spatial right-of-way. The study’s calculations can also be used to improve other types of roads that can take a median separation facility.

User expectations for an automated motorway access assistant system

IntroductionTo avoid the uncertainty present in a cooperation between manual vehicles (MVs) and automated vehicles (AVs), cooperative connected automated mobility (CCAM) is frequently proposed. In this contribution, we consider the situation of a motorway slip road where an AV (SAE level 3 or higher) on the motorway cooperates by opening a suitable gap for an entering AV. To ensure a meeting of the cooperation partners, speed adjustment by the system might often be necessary.MethodsTo investigate the acceptance of an automated cooperative motorway access assistant a driving simulator study (N = 41) was conducted. Participants drove six laps on a virtual test track in a level 3 AV. Three levels of speed adjustment and two levels of an HMI (with or without HMI) were tested. They experienced the automated cooperative motorway access assistant in two situations. Firstly, when they entered the motorway themselves and benefited from the system, and secondly, when the system reduced their vehicle's speed to allow for cooperation with another vehicle on a motorway slip road ahead.ResultsThe results show that the acceptance of the assistant depends significantly on the magnitude of speed reduction: the greater the speed adjustment, the worse the user rating, and the more often the cooperation was terminated. Obviously, the acceptance depends on the perspective: If the users themselves benefit from the speed adjustment of others, they rate the system significantly better than if they themselves must accept the restriction. Only in the case of a small speed reduction, the assistant is rated as well as if the user benefits from the system. The availability of a human-machine interface (HMI) providing information about the system status influenced acceptance only marginally but had a significant influence on the trust in the automation.DiscussionThe results suggest that people are willing to accept a personal disadvantage in order to give others an advantage in the context of CCAM. However, acceptance depends on the magnitude of the disadvantage. The overall agreement scores of the system with HMI did not differ from those without HMI, although the HMI was found to slightly reduce the termination of cooperation. Three possible reasons for this result, which differs from previous research, are discussed.

Assessing advanced driver assistance systems in police vehicles under demanding conditions

Advanced driver-assistance systems (ADAS) are technologies that can enhance drivers’ safety by relieving them from some driving related activities. However, police driving conditions and demands are different from those of civilian drivers. The objective of this study was to assess the impact of ADAS such as forward collision warning (FCW), automatic emergency braking (AEB), and blind spot monitoring (BSM) on police officers’ driving performance, workload, and trust in vehicle safety to provide personalised solutions for police vehicles. A driving simulation study was conducted with 18 police officers. ADAS use was assessed under various driving conditions and while officers were engaged in non-driving related tasks. Findings suggested that the FCW and AEB systems improved officers’ driving performance, while the BSM system had limited effectiveness due to low salience. ADAS were beneficial under normal driving conditions and when officers were using in-vehicle technology; however, they did not help officers in pursuit conditions.

Effects of ambient temperature on drivers’ speed-selective behavior at different risk levels: A driving simulation study

Abnormal or inappropriate ambient conditions can lead to reduced driving performance or even crashes, and the ambient temperature could be the most critical and fundamental factor. However, the coupling effects of the ambient temperature and the road and traffic conditions on basic speed-selection behavior are rarely investigated. To this end, this study attempted to evaluate drivers’ speed-selective behavior at low-medium-high risk levels under the low (16 °C ± 2 °C), medium (26 °C ± 2 °C), and high (36 °C ± 2 °C) ambient temperature ranges. To achieve this, a series of driving simulation experiments were conducted with 45 participants. Results showed that, at the low-risk level, the limitations of low-temperature or high-temperature conditions resulted in higher average speed than the medium-temperature condition. At the medium-risk level, the driver’s dynamic speed adjustment behavior was degraded under low-temperature or high-temperature conditions, and the deceleration rate was higher than the medium-temperature condition. At the high-risk level, drivers failed to diminish their speed in a timely way under low-temperature or high-temperature conditions, increasing the risk of crashes. However, decreasing the speed at the point of conflict could be effective in reducing the severity of the collision. The findings of this study can be applied to autonomous vehicles (AVs) to search for better safety countermeasures.

Audiovisual messages may improve the processing of traffic information and driver attention during partially automated driving: An EEG study

Partially autonomous vehicles can help minimize human errors. However, being free from some driving subtasks can result in a low vigilance state, which can affect the driver’s attention towards the road. The present study first tested whether drivers of partially autonomous vehicles would benefit from the addition of auditory versions of the messages presented in variable message signs (VMS), particularly, when they find themselves in a monotonous driving situation. A second aim was to test whether the addition of auditory messages would also produce an indirect effect on the driver’s vigilance, improving performance on other driving subtasks not related to the message processing. Forty-three volunteers participated in a driving simulator study. They completed two tasks: (a) a VMS task, where they had to regain manual control of the car if the VMS message was critical, and (b) a car-following task, where they had to pay attention to the preceding car to respond to occasional brake events. Behavioral and EEG data were registered. Overall, results indicated that the addition of audio messages helped drivers process VMS information more effectively and maintain a higher level of vigilance throughout the driving time. These findings would provide useful information for the development of partially automated vehicles, as their design must guarantee that the driver remains attentive enough to assume control when necessary.

What would affect drivers’ stop-and-go decisions at yellow dilemma zones? A driving simulator study in Hong Kong

Yellow dilemma, at which a driver can neither stop nor go safely after the onset of yellow signals, is one of the major crash contributory factors at the signal junctions. Studies have visited the yellow dilemma problem using observation surveys. Factors including road environment, traffic conditions, and driver characteristics that affect the driver behaviours are revealed. However, it is rare that the joint effects of situational and attitudinal factors on the driver behaviours at the yellow dilemma zone are considered. In this study, drivers’ propensity to stop after the onset of yellow signals is examined using the driving simulator approach. For instances, the association between driver propensity, socio-demographics, safety perception, traffic signals, and traffic and weather conditions are measured using a binary logit model. Additionally, variations in the effect of influencing factors on driver behaviours are accommodated by adding the interaction terms for driver characteristics, traffic flow characteristics, traffic signals, and weather conditions. Results indicate that weather conditions, traffic volume, position of yellow dilemma in the sequence, driver age and safety perception significantly affect the drivers’ propensity to stop after the onset of yellow signals. Furthermore, there are remarkable interactions for the effects of driver gender and location of yellow dilemma.

Drivers’ situational awareness of surrounding vehicles during takeovers: Evidence from a driving simulator study

This study aimed to understand the influence of surrounding vehicle configuration, driving lane, and traffic density on drivers’ situational awareness (SA), takeover performance, and eye-tracking behaviors in conditionally automated driving. An experiment was conducted with the participation of 40 university students using a fixed-base driving simulator configured to simulate SAE Level 3 automation. During the experiment, participants were engaged in playing Tetris on a tablet as a non-driving related task in automated driving mode. Upon hearing an auditory takeover request, participants were instructed to take control of the vehicle, and then complete a scene reconstruction task to report their SA after transferring control back to the automated driving system. Our findings showed that drivers often neglected vehicles at their sides and rear during the takeover, which was associated with higher collision risks. Higher oncoming traffic density led to drivers’ worse SA of surrounding vehicles but more cautious driving behavior. Driving in the right lane generally resulted in smoother takeovers with lower collision risks. Interestingly, while SA did moderate the impacts of driving conditions on safety margins, a higher level of SA did not consistently relate to improved performance, especially in complex scenarios. This suggests the need for support systems that guide drivers to focus on safety–critical objects rather than simply amplifying SA in general. These insights have significant implications for the design of driver monitoring and support systems in automated vehicles.

Examining Psychological Conflict-Handling Strategies for Highly Automated Vehicles to Resolve Legal User-Vehicle Conflicts

As vehicle automation progresses, with SAE levels 3 to 5 introducing varying degrees of control transfer from driver to vehicle, a key challenge emerges: aligning driver interests with the automated vehicle's (AV) operational goals. Despite technical feasibility, drivers' tendencies to intervene due to distrust in or dissatisfaction with AVs necessitate consideration of control mechanisms in future AV designs. This study investigates the potential of persuasion strategies inspired by Human-Robot Interaction research to harmonize driver actions with AV goals in legal conflict situations. We conducted a Virtual Reality driving simulator study with 36 participants, comparing 11 persuasive conflict-handling strategies and a baseline of no persuasion. Our research helps understand the effects of persuasion on users' compliance with law-abiding AV goals, trust, and acceptance towards the AV and evaluates the effectiveness of these strategies based on their working mechanism (cognitive, emotional, social, politeness) and valence (negative, neutral, positive). Results show that none of the strategies could substantially increase compliance with the AV, but neutral and positive persuasion strategies did not decrease acceptance and trust towards the AV compared to no persuasion. These findings contribute to the discourse on cooperation design and control allocation in AVs, particularly in scenarios involving legal conflicts.

The Importance of Timing—An Expert Evaluation on Latencies for Voice Assistants

In a driving simulator study, six experts interacted with an in-vehicle voice assistant (VA) and rated different latencies. The results suggest that in order to maintain drivers’ satisfaction with the interaction, in-vehicle VAs should have a latency of no more than 5 s. A slight delay of 1.5 s was rated best while shorter latencies caused highest variance amongst experts—indicating that the fastest response may not necessarily be the most desirable. Satisfaction may also depend on the complexity of the use case as experts showed higher tolerance toward longer latencies in the navigation-domain. Furthermore, our results raise thought-provoking insights about the importance of considering human expectations and preferences in the design of in-vehicle VAs. Despite technological advancements, humans might still expect a natural delay, similar to that in a human-human interaction. These findings emphasize the need to balance using cutting-edge technology with the desire for familiar interactions.

The impact of auditory advanced driver distraction warning devices on the behaviour of middle-aged drivers along urban roads

Driver distraction is one of the main causes of road crashes worldwide, with a relevant number of collisions, some fatal, especially among middle-aged drivers. In 2019, the European Regulation EU 2019/2144 mandated the installation of Advanced Driver Distraction Warning (ADDW) devices on all new vehicles by 2026. After the confirmation of its effectiveness in motorway driving, this driving simulation study evaluated the effectiveness of an auditory ADDW device in mitigating distraction on urban roads. Thirty middle-aged participants (fifteen females, aged between 25 and 35) were asked to read and respond to text messages on their cell phones, regardless of the device alert. They all drove (i) without being distracted (baseline condition), (ii) while distracted, and (iii) while distracted but supported by the ADDW device. Each participant faced three scenarios: (i) interaction with pedestrians at a mid-block crosswalk, (ii) driving in free-flow conditions on a dual-carriageway arterial, and (iii) driving behind another vehicle.The results revealed a degradation in driving performance and worsening safety conditions. In contrast to what was demonstrated for motorway driving, the ADDW device did not produce significant improvements in driving performance along urban roads. For drivers who continue to text while driving, no positive effect of the ADDW auditory device was observed. Unlike the motorway scenario, the urban environment produces a variety of stimuli that render distracted driving dangerous irrespective of the use of an ADDW.

Driving Behavior Characteristics of Merging Sections in the Urban Underground Road Junction: A Driving Simulator Study

Driving Behavior Characteristics of Merging Sections in the Urban Underground Road Junction: A Driving Simulator Study

Joining SUMO and Unreal Engine to Create a Bespoke 360 Degree Narrow Passage Driving Simulator

The use of simulators is widespread in driver behavioural research. The ability of driving simulators to achieve the high levels of behavioural fidelity desired by behavioural researchers is argued to be resultant of the physical fidelity of the simulator. Whilst attempts to maximise the physical fidelity of simulators have often been focused on the hardware capabilities of the simulator, the software of the simulator has been argued to be as important. This is because the software of a simulator controls the intelligence and the heterogeneity of the behaviours of the simulated vehicles, as well as the quality of the graphics of the simulation. Despite the importance of intelligent simulated agents, previous driving simulator studies have tended to simplify the behaviours of simulated vehicles and the scenarios that are presented to participants. This is particularly true of simulator studies investigating the decision-making of drivers at narrow passages, a relatively unregulated but hazardous situation in which two opposing vehicles must negotiate how to safely pass through a road narrowing, in which the interactive nature of the interaction has often been neglected. Following a review of the requirements for a representative narrow passage driving simulator, it is argued that co-simulation, an approach which combines multiple simulator types to create a global simulation, provides the best approach to creating intelligent simulated agents within an immersive environment for narrow passage behavioural research. As such, the development of a simulator for narrow passage behavioural research that combines SUMO and Unreal Engine is described. In particular, the development of a novel narrow passage behavioural model within SUMO that utilises previous behavioural findings is highlighted. To this end, it is argued that this approach facilitates higher levels of behavioural fidelity for narrow passage interaction studies and provides a framework for the investigation of other driver behaviours.

What is good? Exploring the applicability of a one item measure as a proxy for measuring acceptance in driver-vehicle interaction studies

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.

A Driving Simulator Study to Examine the Impact of Visual Distraction Duration from In-Vehicle Displays: Driving Performance, Detection Response, and Mental Workload

This research explores the impact of visual distraction duration from multifunctional in-car displays on driver safety. Utilizing a driving simulator and eye-tracking technology, this study involved 35 participants in visual search and car-following tasks, assessing their performance and mental workload across different durations of distraction. The results show that distractions lead to a decrease in driving control and a rise in mental workload, characterized by deteriorated vehicle handling and longer reaction times. With continued exposure to distractions, drivers begin to adapt, indicating a non-linear relationship between the duration of distraction and its consequences. This adaptation points to a threshold beyond which the negative effects of distractions no longer intensify. This work aids in developing safer automotive interfaces by highlighting the effects of larger screen trends on driving behavior and proposing strategies to mitigate distractions. It enriches the discourse on human–machine interaction by offering fresh perspectives on how visual distraction duration from in-car displays influences driving dynamics and cognitive load, thereby enhancing road safety.

Training benefits driver behaviour while using automation with an attention monitoring system

Attention, or more generally, driver monitoring systems have been identified as a necessity to address overreliance on driving automation. However, research suggests that monitoring systems may not be sufficient to support safe use of advanced driver assistance systems (ADAS), also evidenced by a recent major recall of Tesla’s monitoring software. The objective of the current study was to investigate whether different training approaches improve driver behaviour while using ADAS with an attention monitoring system. A driving simulator study was conducted with three between-subject groups: no training, limitation-focused training (highlighted situations where ADAS would not work), and responsibility-focused training (highlighted the driver’s role/responsibility while using ADAS). All participants (N = 47) experienced eight events which required the ego-vehicle to slow down to avoid a collision. Anticipatory cues in the environment indicated the potential for the upcoming events. Event type (covered in training vs. not covered) and event criticality (action-necessary vs. action-not-necessary) were within-subject factors. The responsibility-focused group made fewer long glances (≥ 3 s) to a secondary task than the no training and limitation-focused groups when there were no anticipatory cues. Responsibility-focused training and no training were associated with faster takeover time at the events than limitation-focused training. There were additional benefits of responsibility-focused training for events that were covered in training (e.g., higher percent of time looking at the anticipatory cues). Overall, our results suggest that even if attention monitoring systems are implemented, there may be benefits to driver ADAS training. Responsibility-focused training may be preferable to limitation-focused training, especially for situations where minimizing training length is advantageous.

EHMI on the Vehicle or on the Infrastructure? A Driving Simulator Study

Automated vehicles (AVs) may require the implementation of an external human-machine interface (eHMI) to communicate their intentions to human-driven vehicles. The optimal placement of the eHMI, either on the AV itself or as part of the road infrastructure, remains undetermined. The current driving simulator study investigated the effect of eHMI positioning on human driving behavior, during the approach and execution of right turns at T-intersections. Forty-three participants drove under three conditions: absence of eHMI, eHMI on the AV (eHMIv), and eHMI integrated into the infrastructure (eHMIi). Participants encountered AVs that either yielded or did not yield to their vehicles. The results regarding the placement of the eHMI showed that both concepts are advantageous, but for different reasons. eHMIv was appreciated because implicit and explicit communication are congruent, although the AV must first be visually identified to respond to it. eHMIi was appreciated because a familiar cue is always at a known location in the environment; as a result, participants braked earlier for the intersection and came less close to the AV (which can be interpreted as a safety advantage or efficiency disadvantage). Although there are limitations to a driving simulator study like this, this research provides important insights into the fundamental question of how information placement affects drivers’ visual attention demands and driving behavior, topics that are important in view of the development of future cities.