Driving Simulator Environment Research Articles

Evaluating the effectiveness of engineering countermeasures at yellow dilemma zone: a driving simulator-based study

ABSTRACT The indecisiveness of drivers at the yellow onset creates a dilemma zone (DZ) upstream of the signalized crossings. The yellow light dilemma negatively influences intersection safety, often leading to rear-end and right-angle collisions. This study explores the impact of upstream road marking and active in-vehicle warnings designed based on DZ boundaries. The prototype concepts of in-vehicle warning systems and upstream road marking were designed and implemented in the driving simulator environment. The driver responses from the experimental setup were captured to evaluate the effectiveness of the designed DZ countermeasures. Study results indicate that implemented DZ countermeasures (i.e. DZ road marking, in-vehicle display warning, and in-vehicle audio warning) significantly decreased the drivers’ crossing probability, resulting in a shorter dilemma zone. However, the proposed countermeasures did not show any improvement in the drivers’ red-light running (RLR) behavior. The driving simulator study showed promising results in reducing aggressive crossing responses at the yellow onset.

Driving Distraction Evaluation Model of In-Vehicle Infotainment Systems Based on Driving Performance and Visual Characteristics

With the rapid development of automotive electronic technology, increasingly car enterprises choose to carry touchscreen in-vehicle infotainment systems (IVISs) in smart cars. The integration of navigation, music, telephone, and other functions into touchscreen IVISs has greatly enriched the driving experience. However, the design of touchscreen IVISs, which lacks consideration of driver human–computer interaction leads to increased driving distraction. The current discussion is limited to evaluating the overall distractions of the secondary task, and there is a lack of research on classifying and summarizing the types of driving distractions. Therefore, this paper proposes a new method to evaluate the degree of driving distraction during the interaction with touchscreen IVISs based on driving performance and visual characteristics. A driving simulation environment was built to conduct psychological experiments on three types of driving distraction, namely visual, manual, and cognitive distraction, respectively, to obtain the impact of different types of driving distraction by using touchscreen IVISs. Combining the criteria importance through intercriteria correlation method with fuzzy comprehensive evaluation to establish a driving distraction evaluation method, we quantify the driving distraction caused by the touch screen IVIS. The results of this study indicate that the proposed driving distraction evaluation model by using driving performance and visual characteristics can effectively evaluate the degree of driving distraction caused by the touchscreen IVIS and provide reference and specification for IVIS safety design from a human–computer interaction perspective.

The Impact of Environmental Features on Drivers’ Situation Awareness Using Real-World Driving Scenarios

Advanced driver assistance systems (ADAS) need to account for the driver’s awareness of the environment to be effectively used. This study examines the impact of environmental features (eg, visual complexity, object density, roadway type, lighting) on drivers’ situation awareness (SA). This is achieved using a controlled study with 40 participants. Using a split-plot design, the participants were shown 30 out of 75 real-world driving scenarios displayed in a driving simulator environment. Participants’ responses to Situational Awareness Global Assessment Technique (SAGAT) queries on the type and coordinates of objects in the scene were used to calculate SA scores. A hurdle model was developed to estimate participants’ SA scores. The key findings highlight visual complexity as a significant predictor of SA scores. This predictor was easy to compute and able to capture the complexity of objects that impact road safety as well as the visual clutter in the background. The model showed that drivers were able to identify at least one object of interest in complex environments with high visual complexity and with many objects. A higher proportion of vulnerable road users was associated with a greater likelihood of a non-zero SA score, but the SA score was lower compared to environments with higher proportions of cars. The findings of this study provide insights into the environmental factors to be considered for SA predictive models.

Comparing Eye-tracking System Effectiveness in Field and Driving Simulator Studies

Background: Several studies have been conducted by combining the benefits of eye-tracking systems with driving simulators to simultaneously investigate driving behavior and the potential source of distraction. However, little effort has been made in terms of eye-tracking validation in the driving simulator environment. Objective: The overall aim of this study is to validate an eye-tracking system within the context of a driving simulation environment by considering a specific urban context application. Methods: A field survey and a driving simulation experiment were conducted for a case study in Rome, Italy. The selected real road sections and events were reproduced on the driving simulator system. An eye-tracking system was used to record eye movements both on a real vehicle and the simulator. The eye movements of 14 participants in the field survey and 18 participants in the driving simulation tests, as well as their driving performances, were collected while approaching an urban intersection in relation to two specific road events: i) the presence of a speed limit sign and ii) the presence of a crossing pedestrian. Results: Eye tracker parameters and driving performances were compared between the real driving tests and driving simulator experiments in order to validate the eye-tracking system. It was validated for both events in terms of duration and distance of the eye fixation. Conclusion: The results demonstrated that an eye-tracking system is an effective tool for studies and applications in a virtual reality environment.

Lexicographic Actor-Critic Deep Reinforcement Learning for Urban Autonomous Driving

Urban autonomous driving is a difficult task because of its complex road scenarios and the interaction between multiple vehicles. Autonomous vehicles need to balance multiple objectives in these complex scenarios, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , safety and speed. Traditional reinforcement learning methods deal with the multi-objective problem by optimizing agents with a single objective reward. However, these methods are sensitive to the reward scale and require huge experiments to design reward weights. In this paper, we propose the Lexicographical Proximal Policy Optimization algorithm (LPPO), which can express people's preference relationship through the lexicographical ordering between objectives. The proposed method has two main advantages. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">On the one hand,</i> LPPO has a smaller parameter adjustment space, which makes it easy to find the optimal solution that satisfies the actual problem preference. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">On the other hand,</i> the proposed method is less affected by the reward scale and easy to deploy in various driving scenarios. We evaluate our algorithm in two driving simulation environments, and the results show that the proposed method has better performance in urban driving tasks than previous reinforcement learning algorithms. In addition, we illustrate that the proposed method has better stability even if the reward scale changes.

Impact of In-Air Gestures on In-Car Task’s Diver Distraction

As in-vehicle information systems (IVIS) grow increasingly complex, the demand for innovative artificial intelligence-based interaction methods that enhance cybersecurity becomes more crucial. In-air gestures offer a promising solution due to their intuitiveness and individual uniqueness, potentially improving security in human–computer interactions. However, the impact of in-air gestures on driver distraction during in-vehicle tasks and the scarcity of skeleton-based in-air gesture recognition methods in IVIS remain largely unexplored. To address these challenges, we developed a skeleton-based framework specifically tailored for IVIS that recognizes in-air gestures, classifying them as static or dynamic. Our gesture model, tested on the large-scale AUTSL dataset, demonstrates accuracy comparable to state-of-the-art methods and increased efficiency on mobile devices. In comparative experiments between in-air gestures and touch interactions within a driving simulation environment, we established an evaluation system to assess the driver’s attention level during driving. Our findings indicate that in-air gestures provide a more efficient and less distracting interaction solution for IVIS in multi-goal driving environments, significantly improving driving performance by 65%. The proposed framework can serve as a valuable tool for designing future in-air gesture-based interfaces for IVIS, contributing to enhanced cybersecurity.

Round-trip driving effects on driving performances and mental workload under different traffic rules

This study investigates the effect of switching between different traffic rules (left-versus right-hand traffic) on driving performance and mental workload. A driving simulation environment was developed according to the real environment. Two urban roads with different traffic systems were simulated. Twenty participants executed intersection turns and continuous car-following behavior in four simulated driving stages, including driving with familiar, unfamiliar, second time unfamiliar, and back to familiar traffic rules. The mean and standard deviations for speed, distance headway, and the standard deviation of lateral position were recorded as driving performance. Mental workload was determined using the NASA-TLX and Rating Scale Mental Effort questionnaires. One-way analysis of variance was used to evaluate the differences between the four driving stages using subjective and objective measures. The results showed that significant differences were obtained in all measures when driving in the four driving situations, except for the speed standard deviation. The car-following behavior was the most unsafe (significantly larger standard deviations for distance headway and mental workload) when driving in unfamiliar road traffic compared with the other stages. When driving under unfamiliar traffic rules for the second time, the mental workload was significantly relieved and the lane-keeping ability significantly improved. The results indicated that providing an adaptive runway for drivers to familiarize themselves with different traffic rules is necessary to improve driving performance and safety. These findings provide useful information for designing bridges linking two places with different traffic rules to increase traffic safety.

Promotion of driver compliance with V2V information in car-following tasks via multimodal display

The success of vehicle-to-vehicle (V2V) communication in improving the driving safety and efficiency primarily depends on driver compliance with the transmitted information. In this study, driver compliance with V2V information in simulated car-following tasks was measured and analyzed under different information display settings, such as baseline without information, visual information only, and multimodal information. Car-following data were collected from 30 participants in a driving simulation environment. The results revealed that V2V information could significantly reduce the driver's response time to the lead car’s speed change, resulting in an increase in the minimum time-to-collision when the lead car slowed down. Driver compliance was significantly higher under multimodal information settings than when using visual information alone. Moreover, driver compliance was higher when facing a decelerating lead car than when facing an accelerating lead car. No significant differences in driver compliance between the deceleration levels was observed; however, differences existed among different car-following preference groups. The findings of this study can be applied to V2V car-following models to improve the simulation accuracy for connected-vehicle traffic. Additionally, multimodal information presentation can provide a reference for V2V communication design.

On Determinism of Game Engines Used for Simulation-Based Autonomous Vehicle Verification

Game engines are increasingly used as simulation platforms by the autonomous vehicle community to develop vehicle control systems and test environments. A key requirement for simulation-based development and verification is determinism, since a deterministic process will always produce the same output given the same initial conditions and event history. Thus, in a deterministic simulation environment, tests are rendered repeatable and yield simulation results that are trustworthy and straightforward to debug. However, game engines are seldom deterministic. This paper reviews and identifies the potential causes and effects of non-deterministic behaviours in game engines. A case study using CARLA, an open-source autonomous driving simulation environment powered by Unreal Engine, is presented to highlight its inherent shortcomings in providing sufficient precision in experimental results. Different configurations and utilisations of the software and hardware are explored to determine an operational domain where the simulation precision is sufficiently high i.e. variance between repeated executions becomes negligible for development and testing work. Finally, a method of a general nature is proposed, that can be used to find the domains of permissible variance in game engine simulations for any given system configuration.

Driving Simulator as a Tool for Reaction Time Measurement

The reaction times of a driver were measured in a driving simulator environment in this article. Young drivers under the age of 26 were the focus of the study. They cause many accidents. Each participating driver provided basic information later used for mathematical-statistical analysis. The main advantage of driving simulators is limitless usage. It is possible to simulate situations that would be unacceptable in real road traffic. Therefore, this study could focus also on drivers influenced by alcohol. The main goal of the article is to evaluate the reaction times. Then it was possible to evaluate if gender, practice, and alcohol, statistically significantly affect the reaction time. We also focused on drinking before driving for a smaller number of drivers. For these mathematical-statistical purposes, we used a one-sample t-test, paired-samples t-test, and independent-sample t-test.

Evaluating the cognitive and psychological effects of real-time auditory travel information on drivers using EEG

ABSTRACT Real-time travel information design with inadequate consideration of human factors can lead to driver distraction and diminish road safety. This study measures drivers’ brain electrical activity patterns to evaluate multiple aspects of driver cognition and psychology under real-time information provision using insights from the neuroscience domain on the localisation of brain functions. The brain electrical activity patterns of 84 participants are collected using an electroencephalogram (EEG) in an interactive driving simulator environment. The impacts of real-time auditory travel information characteristics (amount, sufficiency, and content) and different time stages of interaction with information provision (before, during, and after) on the frequency band powers of EEG signals in different brain regions are analyzed using linear mixed models. Study results illustrate that drivers exert more cognitive effort to perceive/process information on routes with complex driving environments. Insufficient information may evoke increased attention to internal processing and memory processing on routes characterised by higher travel time uncertainty, while route recommendation to switch to such routes may increase drivers’ stress and anxiety. The study findings can aid information providers, both private and public, as well as auto manufacturers to incorporate driver cognition in designing safer real-time information and their delivery systems.

Evaluation of Driver's Reaction Time Measured in Driving Simulator.

This article evaluates the driver’s reaction times in a driving simulator environment. The research focused mainly on young drivers under the age of 26, who cause many accidents. Each participating driver provided basic information later used for mathematical-statistical analysis. The main advantage of driving simulators is limitless usage. It is possible to simulate situations that would be unacceptable in real road traffic. Therefore, this study is also able to examine drunk driving. The main goal of the article is to evaluate if gender, practice, or alcohol significantly affected the reaction time of 30 drivers. We also focused on drinking before driving for a smaller number of the drivers; ten of them performed driving under the influence of alcohol. For these mathematical-statistical purposes, we used a one-sample t-test, a paired-samples t-test, an independent-sample t-test, and a correlation analysis together with the assessment of its statistical significance.

Benchmarking Visual Performance with Monofocal Intraocular Lenses with and without Enhanced Optical Properties in a Nighttime Driving Simulator Environment: A Proof-of-Concept Study.

The purpose of this study is to introduce a method for benchmarking intraocular lenses during driving activities under highly standardized conditions, specifically with regard to visual acuity (VA) and contrast sensitivity (CS). Therefore, patients with intraocular lens (IOL) implants ICB00 (Tecnis Eyhance, Johnson & Johnson, Santa Ana, CA, USA) vs. CNA0T0 (Clareon, Alcon Laboratories Inc., Fort Worth, TX, USA) were examined clinically and in a nighttime driving simulator. Clinical tests for high (HCVA), low contrast (LCVA) distant VA, and mesopic CS were conducted in subjects at least 2 months after binocular IOL surgery (inclusion criteria: VA > 20/25, ophthalmologically normal, except cataract surgery). All patients completed a straight driving simulator route. VA, CS, and halo size were assessed binocularly during nighttime driving using eight-position Landolt Cs in four different locations and two (far and intermediate) distances. Results are presented as median/interquartile range: ICB00 data (corresponding CNA0T0 data are shown in brackets): 5 (6) subjects, aged 69.6/8.3 (71.1/13.0) years were enrolled. Clinical tests: logMAR HCVA 0.11/0.39 (0.00/0.51), logMAR LCVA 0.78/0.52 (0.80/0.54); logCS without glare 0.50/0.31 (0.30/0.65), logCS with glare 0.20/0.15 (0.20/0.5). Nighttime driving simulator: logMAR VA thresholds for right roadside, dashboard, navigation screen, and interior rear-view mirror were 0.50/0.06 (0.57/0.22), 0.81/0.07 (0.91/0.14), 0.80/0.17 (0.92/0.27), 0.50/0.11 (0.63/0.26); logCS thresholds were1.53/0.67 (1.00/0.81), 0.82/0.11 (0.61/0.19), 0.71/0.14 (0.50/0.15), 0.87/0.07 (0.81/0.11). Halo size: 5.40°/0.89° (5.88°/2.00°). Within a nighttime driving simulator environment, ICB00 exceeded CNA0T0 in median logMAR (VA) and logCS by 0.1 log unit at intermediate distances (dashboard, navigation screen). The clinical test for far and remote distances did not show a difference. These results confirm benefits of monofocal IOLs with enhanced optical properties for intermediate distances compared to conventional monofocal lenses within the target medium distance ranges.

Strangers in a Strange Land: New Experimental System for Understanding Driving Culture Using VR

In order for autonomous vehicles to adapt to local norms in human driving, it is critical to profile how human driving differs across geographical locations. While ethnographers have qualitatively described regional differences in driving style, data-driven statistical models might help computer-driven cars drive like locals and recognize how local drivers might be signaling through hand/body movement and motion of their vehicles. To this end, we have created an experimental system and method to profile driving behavior and interaction using a multi-participant virtual reality (VR) driving simulation environment. The system was designed to be portable and to support cross-cultural experimental deployments. We aim to make sure the system is operational and functional, can model diverse scenarios, generates data fit for analysis, and captures expected behaviors. We describe the system, test scenarios, and findings of the proof-of-concept study conducted in the U.S. and Israel.

Promotion of cooperative lane changes by use of emotional vehicle-to-vehicle communication

This driving simulator study aimed to encourage cooperative lane changes through vehicle-to-vehicle (V2V) communication and explore whether emotional or rational communication content is better in promoting cooperative lane change. A total of 960 lane-changing datapoints from 30 participants in a driving simulation environment were collected. The participants’ behavior, driving-related data, and emotional responses were recorded and analyzed. The results revealed that the trigger time to collision (TTC) between the lane changer and the following vehicle in the target lane and communication types were all important factors influencing the willingness of drivers to cooperate. V2V communication could significantly increase the willingness of the driver in the following vehicle to cooperate compared to the traditional method in which desire to change lanes is conveyed with only turn lights. The effect of different communication contents on willingness to cooperate did not vary significantly; however, emotional communication was superior to rational communication in some cases. This indicates that changing lanes owing to an emergency was more likely to be successful. The results of this study can provide a reference for V2V communication design for a safer and more comfortable driving experience.

PADA-AD Test - a standardized test method for assessing takeover performance of drivers during automated driving

The presented method describes a standardized test procedure for the evaluation of takeover performance of drivers during automated driving. It was primarily developed to be used for evaluating Level 3 systems (conditional automated driving). It should be applied in a driving simulator environment during the development phase of a system. The method consists of a test course on a three-lane highway with 12 test scenarios where the driver repeatedly has to take over control from the automated system. Each scenario requires the driver to build up an adequate situation awareness in order to take the decision for the correct action. The method was explicitly designed to map the four relevant steps in the takeover process of automated driving: Perception – Awareness – Decision – Action and is therefore called PADA-AD Test for automated driving. The method description contains guidelines with regard to the specification of the test course and the included test scenarios, the design and duration of the automated drives, the non-driving related task to be performed during the automated drives, the instructions to be given to the subjects and finally the measures for evaluating takeover performance of the drivers.• A test procedure for the evaluation of takeover performance of drivers during automated driving was developed for usage in a driving simulator during the development phase of a system/HMI•The test course enables the assessment of the driver's takeover performance in various test scenarios including higher cognitive processes•The method is highly standardized and thus replicable through use of a predetermined test course with clearly defined scenarios, reduced environmental conditions and "popping up” of situational elements.

Improving Driving Safety by Detecting Negative Emotions with Biological Signals: Which Is the Best?

There is ample evidence confirming the adverse effects of negative emotions such as anger, fear, and anxiety on drivers’ performance. Also, effectiveness of biological signals in emotion recognition has been confirmed. Therefore, developing advanced driver-assistance systems based on biological signals to detect negative emotions can play a major role in improving driving safety. However, since recording signals, data analysis, as well as design and implementation of a system based on one or more biological signals take time and are costly, it is necessary to conduct appropriate preliminary studies on the efficiency of these signals in identifying negative emotions. The purpose of this study was to explore the efficiency of four biological signals including electrocardiogram (ECG), electromyogram (EMG), electrodermal activity (EDA), and electroencephalogram (EEG) in detecting negative emotions while driving. To this end, a series of scenarios were designed to arouse negative emotions in the driving simulator environment. A total of 43 individuals participated in the experiments, during which the four signals were recorded. Next, we extracted 58 features from the collected data for analysis. Then, multi-layer perceptron and radial basis function neural networks were implemented using the features of each of these signals separately. Afterward, the four evaluation criteria of accuracy, sensitivity, specificity, and precision were calculated for the signals. Finally, TOPSIS was used to rank the signals. ECG and EDA signals, with 88% and 90% accuracy, respectively, were found to be the best signals in detecting negative emotions during driving.

The Impact of LED Colour Rendering on Reaction Time of Human Eyes in Tunnel Interior Zone

The long reaction time of human eyes will increase the probability of traffic accidents in tunnels which can be reduced by improving lighting conditions. In recent years, light-emitting diodes (LEDs) have replaced the traditional lighting source in a tunnel. Colour rendering index 2012 (CRI2012, a colour rendering evaluation index) is the most suitable evaluation method for colour rendering of LEDs. In order to study the impact of colour rendering of LEDs on the reaction time of human eyes, a driving simulation environment was designed. First, three CCTs (correlated colour temperatures), four CRI2012s, and eight colours of targets were assessed using mesopic photometry model MES-2. The calculation results indicated that the contrast of targets had a positive correlation with CRI2012 and had a negative correlation with CCT. Then, reaction time experiment was conducted in simulated tunnel environments. There are 30 observers who participated. The results show that the LEDs with higher CRI2012 and lower CCT in tunnel is conducive to reducing the reaction time of human eyes. High CRI2012 enables drivers to accurately and quickly identify colours. This paper provides a reference for the design of light source parameters, roads, walls, and warning signs in the tunnel.

When the automated driving system fails: Dynamics of public responses to automated vehicles

Automated vehicles (AVs) will be at the stage of human–machine cooperative driving probably for a long term and their control is shared by two drivers: the human driver and the automated driving system (ADS). ADS failures are a major contributor to the disengagements of AV testing on public roads and influence human–ADS interactions. The impact of ADS failures (malfunctions or limits) was only examined in the driving simulator environment and mainly on self-reported trust in the ADS or AVs. Utilizing simulators to investigate the influence of ADS failures might lack ecological validity. Participants in this environment might find it hard to imagine the risk of ADS failures and the criticality of taking immediate manual control. We conducted a field study with a real Level 3 AV and a large-scale test track. We invited participants (N = 261) to experience the AV and an ADS malfunction as passengers in a naturalistic driving environment, exposing almost half the participants to the malfunction. Witnessing the malfunction caused participants’ reductions in six psychological responses to AVs and ADS (trust, positive affect, perceived usefulness, perceived ease of use, attitude, and behavioral intention) and increased negative affect. This finding highlights the importance of mitigating the detrimental influences of ADS malfunctions. Trust repair deserves consideration in maintaining adequate human–machine cooperation in AVs after ADS malfunctions.

Implementing a Gaze Tracking Algorithm for Improving Advanced Driver Assistance Systems

Car accidents are one of the top ten causes of death and are produced mainly by driver distractions. ADAS (Advanced Driver Assistance Systems) can warn the driver of dangerous scenarios, improving road safety, and reducing the number of traffic accidents. However, having a system that is continuously sounding alarms can be overwhelming or confusing or both, and can be counterproductive. Using the driver’s attention to build an efficient ADAS is the main contribution of this work. To obtain this “attention value” the use of a Gaze tracking is proposed. Driver’s gaze direction is a crucial factor in understanding fatal distractions, as well as discerning when it is necessary to warn the driver about risks on the road. In this paper, a real-time gaze tracking system is proposed as part of the development of an ADAS that obtains and communicates the driver’s gaze information. The developed ADAS uses gaze information to determine if the drivers are looking to the road with their full attention. This work gives a step ahead in the ADAS based on the driver, building an ADAS that warns the driver only in case of distraction. The gaze tracking system was implemented as a model-based system using a Kinect v2.0 sensor and was adjusted on a set-up environment and tested on a suitable-features driving simulation environment. The average obtained results are promising, having hit ratios between 96.37% and 81.84%.