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Autonomous driving requires robust and highly accurate perception technologies. Various deep learning algorithms based on only image processing satisfy this requirement, but few such algorithms are based on LiDAR. However, images are only one part of the perceptible sensors in an autonomous driving vehicle; LiDAR is also essential for the recognition of driving environments. The main reason why there exist few deep learning algorithms based on LiDAR is a lack of data. Recent translation technology using generative adversarial networks (GANs) has been proposed to deal with this problem. However, these technologies focus on only image-to-image translation, although a lack of data occurs more often with LiDAR than with images. LiDAR translation technology is required not only for data augmentation, but also for driving simulation, which allows algorithms to practice driving as if they were commanding a real vehicle, before doing so in the real world. In other words, driving simulation is a key technology for evaluating and verifying algorithms which are practically applied to vehicles. In this paper, we propose a GAN-based LiDAR translation algorithm for autonomous driving and driving simulation. It is the first LiDAR translation approach that can deal with various types of weather that are based on an empirical approach. We tested the proposed method on the JARI data set, which was collected under various adverse weather scenarios with diverse precipitation and visible distance settings. The proposed method was also applied to the real-world Spain data set. Our experimental results demonstrate that the proposed method can generate realistic LiDAR data under adverse weather conditions.

The present paper focuses on vehicle simulator fidelity, particularly the effect of motion cues intensity on driver performance. The 6-DOF motion platform was used in the experiment; however, we mainly focused on one characteristic of driving behavior. The braking performance of 24 participants in a car simulator was recorded and analyzed. The experiment scenario was composed of acceleration to 120 km/h followed by smooth deceleration to a stop line with prior warning signs at distances of 240, 160, and 80 m to the finish line. To assess the effect of the motion cues, each driver performed the run three times with different motion platform settings–no motion, moderate motion, and maximal possible response and range. The results from the driving simulator were compared with data acquired in an equivalent driving scenario performed in real conditions on a polygon track and taken as reference data. The driving simulator and real car accelerations were recorded using the Xsens MTi-G sensor. The outcomes confirmed the hypothesis that driving with a higher level of motion cues in the driving simulator brought more natural braking behavior of the experimental drivers, better correlated with the real car driving test data, although exceptions were found.

RationaleAlthough driving simulators (DS) are receiving increasing attention due to concern over traffic accidents under the influences of drugs, few DS are recognized for their reliability and validity. Therefore, the development of an evaluation system using DS for driving performance is urgently needed.ObjectivesTo investigate whether the standard deviation of lateral position (SDLP) increases with blood alcohol concentration (BAC) using a DS with reliability and calculate the SDLP threshold from the difference between BAC levels of 0 and 0.05%.MethodsTwenty healthy Japanese men performed the DS tasks up to 60 min in Study 1 and DS tasks twice at 1-week intervals in Study 2. Twenty-six healthy men conducted the same DS tasks under BAC level (0, 0.025, 0.05, and 0.09%) in double-blind, randomized, crossover trial in Study 3. The primary outcome was SDLP in a road-tracking test. The test–retest reliability of DS data was assessed, and the estimated difference in SDLP between BAC levels of 0 and 0.05% was calculated using a linear regression model.ResultsThe cumulative SDLP values at 5-min intervals were stable, and the intraclass correlation coefficient for its values was 0.93. SDLP increased with BAC in a concentration-dependent manner. The predicted ΔSDLP value for the difference between BAC levels of 0 and 0.05% was 9.23 cm. No participants dropped out because of simulator sickness.ConclusionsThe new DS used in these studies has reliability, validity, and tolerability and is considered suitable for evaluating the influence of drugs on driving performance.

Driving simulators are increasingly being incorporated by driving schools into a training process for a variety of vehicles. The motion platform is a major component integrated into simulators to enhance the sense of presence and fidelity of the driving simulator. However, less effort has been devoted to assessing the motion cues feedback on trainee performance in simulators. To address this gap, we thoroughly study the impact of motion cues on braking at a target point as an elementary behavior that reflects the overall driver’s performance. In this paper, we use an eye-tracking device to evaluate driver behavior in addition to evaluating data from a driving simulator and considering participants’ feedback. Furthermore, we compare the effect of different motion levels (“No motion”, “Mild motion”, and “Full motion”) in two road scenarios: with and without the pre-braking warning signs with the speed feedback given by the speedometer. The results showed that a full level of motion cues had a positive effect on braking smoothness and gaze fixation on the track. In particular, the presence of full motion cues helped the participants to gradually decelerate from 5 to 0 ms−1 in the last 240 m before the stop line in both scenarios, without and with warning signs, compared to the hardest braking from 25 to 0 ms−1 produced under the no motion cues conditions. Moreover, the results showed that a combination of the mild motion conditions and warning signs led to an underestimation of the actual speed and a greater fixation of the gaze on the speedometer. Questionnaire data revealed that 95% of the participants did not suffer from motion sickness symptoms, yet participants’ preferences did not indicate that they were aware of the impact of simulator conditions on their driving behavior.

To investigate the physiological and behavioral comfort of drivers traversing urban underpass tunnels with various spatial structure conditions, a driving simulator experiment was conducted using 3DMAX and SCANeRTM studio software. Three parameters, including the slope, slope length, and height of a tunnel, were selected as research objects to explore the optimal combination of structural parameters in urban underpass tunnels. The heart rate (HR), interbeat (RR) interval, speed, and lane centerline offset value were collected for 30 drivers. Then, a measurement model of the relationship among HR, RR interval, speed, lane centerline offset value, and structural parameters was established by using partial correlation analyses and the stepwise regression method. On this basis, a structural constraint model based on the drivers’ physiological and behavioral comfort thresholds was also constructed. The results show that the driver’s HR, RR interval, speed, and lane centerline offsets are significantly related to the tunnel height, slope, and slope length. More importantly, this paper not only analyzed the effects of various structural parameters on drivers’ physiology and behavior but also proposed an optimized combination of structural parameters based on drivers’ physiological and behavioral comfort. It can reasonably improve tunnel design in China, ensure tunnel traffic safety, and seek the maximum comfort of the driver in the driving process.

Vehicle safety remains a topic of major interest, and diverse assistance systems are implemented that focus primarily on analyzing the immediate vicinity of the car and the driver’s control inputs. In this paper, by contrast, we emphasize understanding the driver’s control performance via obtaining valuable data and relevant characteristics. To acquire the data, we employed an in-house-designed, laboratory-built vehicle driving simulator. This simulator exploits the Unreal Engine 4 framework to deliver a high level of realism. The fact that the actual designing and associated processes were materialized through our own efforts has brought advantages such as simplified data acquisition, possibility of creating custom scenarios, and modification of the virtual elements according to our specific needs. We also developed an application to analyze the measured data from the perspective of control theory, establishing a set of parameters that provided the basis for an early version of a driver performance index indicator.

This study aims to investigate the effects of whole-body vibration (WBV) exposure on the disturbance of standing balance function assuming that the cause of slip, trip and fall accidents in the land transportation industry is related to WBV exposure when traveling in vehicles. In the experiment, ten participants underwent 60 min of virtual driving in a driving simulator (DS) for WBV exposure. In addition, standing balance measurements were conducted before exposure, immediately after exposure, 2 min after exposure and 4 min after exposure. Four conditions were considered by combining two magnitudes of WBV exposure and the driver and passenger conditions. This study focused on two indexes of standing balance, namely, total length and enveloped area and the rate of change relative to the value before the vibration exposure was calculated. The rate of change remained almost constant at 1.0 in the control condition without vibration exposure, whereas that under vibration exposure conditions varied. Interestingly, the rate of change at 2 min after exposure remained high in the driver condition, but it decreased to almost 1.0 in the passenger condition. Since no difference appeared in the vibration acceleration measured at the seating surface between the driver and passenger conditions, it was believed that the difference between the driving and passenger conditions was related to fatigue caused by the accelerator-pedal operation. As a result of considering the percentage of the standing balance that returned to 1.0 after 4 min in most conditions, this study proposed that a rest period of several minutes be allowed from the experiment in which the participants were exposed to vibration at 0.5m/s2 rms for 60 min at the DS. Further basic experiments will be conducted to introduce another WBV exposure assessment, including loss of standing balance as a health indicator, to ISO 2631-1.

Urban transportation systems, particularly underground interchanges, present significant challenges for sustainable and resilient urban design due to their complex road geometries and dense traffic signage. These challenges are further compounded by the interaction of diverse road users, which heightens the risk of accidents. To enhance both safety and sustainability, this study integrates advanced driving simulation techniques with machine learning models to improve driving safety and comfort in underground interchanges. By utilizing a driving simulator and 3D modeling, real-world conditions were replicated to design key traffic safety features with an emphasis on sustainability and driver well-being. Critical safety parameters, including speed, acceleration, and pedal use, were analyzed alongside comfort metrics such as lateral acceleration and steering torque. The LightGBM machine learning model was used to classify safety and comfort grades with an accuracy of 97.06%. An important ranking identified entrance signage and deceleration zones as having the greatest impact on safety and comfort, while basic road sections were less influential. These findings underscore the importance of considering visual cues, such as markings and wall color, in creating safer and more comfortable underground road systems. This study’s methodology and results offer valuable insights for urban planners and engineers aiming to design transportation systems that are both safe and aligned with sustainable urban mobility objectives.

Micromobility vehicles, e-scooters and e–bicycles in particular, gain an increasing popularity but also receive criticism, mainly due to road safety issues and their carbon footprint, particularly in relation to their Li-ion batteries. Available field data are not sufficient to explore those issues. Important input variables, such as riders’ reaction time, the impact of human factors on riders’ safety, battery performance degradation with time, remain unknown. This paper presents the design, development, initial calibration and validation of two novel driving simulators, one for an e-scooter and one, for an e-bicycle. The simulators are already operational and used to acquire new knowledge on driving behavior and battery performance. By enabling a better understanding of e-vehicle performance and safety, these simulators contribute to reducing the environmental impact of micromobility by optimizing battery usage and improving vehicle design for sustainability. The paper describes the overall configuration and the main technical specifications of both simulators and provides a thorough description of all their mechanical and electromechanical components. It documents the initial calibration process before launching the experiments and presents the validation methodology with the participation of over 100 users. The outcomes of future experiments are expected to be beneficial to (i) researchers who will gain new insights on e-vehicle performance, (ii) users, enabling them to make informed decisions on vehicle choice and riding patterns, (iii) urban planners on improving urban infrastructure design, (iv) vehicle manufacturers on identifying customer needs and enhancing vehicle design for sustainability, and (v) Public Authorities on adjusting vehicle and infrastructure specifications to reduce the carbon footprint of urban mobility.

IntroductionThis paper builds our knowledge of truck driver behaviour in and experience of automated truck platooning, focusing on the effect of partially and fully automated truck platoons on driver workload, trust, acceptance, performance, and sleepiness.MethodsTwenty-four male drivers experienced three conditions in a truck driving simulator, i.e., baseline, partial automation, and full automation: the baseline condition was driving with standard cruise control; partial automation was automated longitudinal control ten metres behind the truck in front, with the driver having to steer; and full automation was automated longitudinal and lateral control. Each condition was simulated in three situations: light traffic, heavy traffic, and heavy traffic plus fog.ResultsThe experiment demonstrated that automation affects workload. For all workload measures, partial automation produced higher workload than did the full-automation or baseline condition. The two measures capturing trust, i.e., the Human Trust in Automated Systems Scale (HTASS) and Cooper–Harper Scales of Workload, Temporal Load, Situation Awareness, and Trust, were consistent and indicated that trust was highest under the baseline condition, with little difference between partial and full automation. Driver acceptance of both levels of automation was lower than acceptance of baseline. Drivers rated their situation awareness higher for both partial and full automation than for baseline, although both levels of automation led to higher sleepiness.ConclusionsWorkload was higher for partial than for full automation or the baseline condition. Trust and acceptance were generally highest in the baseline condition, and did not differ between partial and full automation. Drivers may believe that they have more situation awareness during automated driving than they actually do. Both levels of automation led to a higher degree of sleepiness than in the baseline condition. The challenge when implementing truck platooning is to develop a system, including human–machine interaction (HMI), that does not overburden the driver, properly addresses driver sleepiness, and satisfies current legislation. The system also must be trusted and accepted by drivers. To achieve this, the development of well-designed HMI will be crucial.