Can We Study Autonomous Driving Comfort in Moving-Base Driving Simulators? A Validation Study
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To lay the basis of studying autonomous driving comfort using driving simulators, we assessed the behavioral validity of two moving-base simulator configurations by contrasting them with a test-track setting.With increasing level of automation, driving comfort becomes increasingly important. Simulators provide a safe environment to study perceived comfort in autonomous driving. To date, however, no studies were conducted in relation to comfort in autonomous driving to determine the extent to which results from simulator studies can be transferred to on-road driving conditions.Participants ( N = 72) experienced six differently parameterized lane-change and deceleration maneuvers and subsequently rated the comfort of each scenario. One group of participants experienced the maneuvers on a test-track setting, whereas two other groups experienced them in one of two moving-base simulator configurations.We could demonstrate relative and absolute validity for one of the two simulator configurations. Subsequent analyses revealed that the validity of the simulator highly depends on the parameterization of the motion system.Moving-base simulation can be a useful research tool to study driving comfort in autonomous vehicles. However, our results point at a preference for subunity scaling factors for both lateral and longitudinal motion cues, which might be explained by an underestimation of speed in virtual environments.In line with previous studies, we recommend lateral- and longitudinal-motion scaling factors of approximately 50% to 60% in order to obtain valid results for both active and passive driving tasks.Keywords:
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Mapping of data obtained on driving simulator to reality based on simulator validation results (WIP)
The use of a driving simulator can be preferable to on-road experiments for cost-efficient rapid prototype testing or dangerous driving scenario evaluations. However, the mismatch of the simulator and the actual driving environment might alter a driver's behavior. This might lead to deceptive results that are not applicable to reality. Therefore, before using the simulator, one must assess its fidelity.In this paper we validate a three-screen desktop simulator by comparing the simulator and on-road tests for a driver's perception of the environment and their driving response. The validation results point to considerable perception alteration and change of driving behavior on the simulator. We propose using the simulator validation results to create a mapping from the simulator domain to the real world to overcome the distortion. The mapping is in the form of a transformation of probability density functions associated with driving response characteristics. In this work we focus on the driving tasks of vehicle following and lane changing to demonstrate the approach.
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Abstract Driving simulators are used to conduct experiments on driver behavior, road design, and vehicle characteristics, etc. It is important that a driving simulator include a realistic simulation of surrounding vehicles in order to be a valid representation of real driving. This paper describes a model that generates and simulates surrounding traffic for a driving simulator. The model is built on established techniques for time-driven micro-simulation of traffic. The model only considers the closest neighborhood of the driving simulator vehicle. This neighborhood is divided into one inner region and two outer regions. Vehicles in the inner region are simulated according to advanced behavioral models while vehicles in the outer regions are updated according to a less time-consuming mesoscopic model. The sub-models for driving behavior are enhanced versions of the sub-models in the HUTSIM/TPMA model and the VTISim model. The developed simulation model also includes a new sub-model for the behavior during overtakings. The developed model has been tested within the VTI Driving simulator III. A driving simulator experiment has been performed in order to check if the participants observe the behavior of the simulated vehicles as realistic or not. The results were promising but they also indicated that enhancements could be made. The model has also been validated on the number of vehicles that catches up with the driving simulator vehicle and vice versa. The agreement is good for active and passive catch-ups on rural roads and for passive catch-ups on freeways, but less good for active catch-ups on freeways.
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Driving simulator is a useful tool for obtaining data on driver behaviour efficiently and quickly. However, to ensure the reliability of the data gathered by the simulator, it is necessary to check the differences between drivers' behaviour in the simulator and in reality. In this study, an existing two-lane rural road was replicated on the driving simulator under the same traffic conditions of groups of cyclists and oncoming motorised vehicles. For this purpose, a naturalistic field data collection was developed on the real road using instrumented bicycles and static video recordings. A total of 30 volunteers participated in the driving simulator tests. The objective validation of the driving simulator was based on three operational variables: average travel speed, overtaking vehicle speed, and lateral clearance. As a result, higher average travel speeds and lower lateral clearances were obtained in the real world compared to those observed in the simulator. It was also found that overtaking vehicle speed depends on the group of cyclists. Overall, the data obtained in the field and in the driving simulator did not present statistically significant differences. The analysis of drivers' perception in the simulator tests concluded that the simulator reflected reality in an accurate way, achieving the subjective validation of the driving simulator. Thus, this study validates the driving simulator for bicycle safety research on rural roads.
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Driving simulators have been widely used in driving behavior analysis and intelligent driving algorithm development. However, the validity of driving behavior data derived from driving simulators remains unclear. In this study, 30 Chinese drivers were recruited to participate in two experiments: on-road and simulator experiments. An instrumented vehicle and a high-fidelity simulator were used in the on-road and simulator experiments, respectively, to investigate the effects of high speed (60, 80, and 100 km/h) and a visual distraction task on the lateral driving performance, including lane deviation (LD), standard deviation of the lane position (SDLP) rate, standard deviation of the steering wheel angle (SDSWA) rate, and steering wheel reversal rates (SRRs) (at levels of 1.3° and 2.5°). It was found that the visual distraction task impaired the drivers' lane-keeping ability. Furthermore, the driving task had similar effects on the LD, SDLP rate, and SRRs (2.5°) in the on-road and simulator experiments. The effects of the driving speed on the LD, SDLP rate, and SDSWA rate were comparable in both driving environments. However, the results confirmed that even a high-fidelity driving simulator could not achieve perfect absolute validity. The results provided preliminary evidence that the high-fidelity driving simulator used in this study might be an effective tool for investigating the effects of visual distractions task on lateral driving behavior.
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A hi-fidelity driving simulator has been developed to measure driving behavior. Since the driver is an important component of Intelligent System (i.e., human factor), it is necessary to measure and investigate the driving behavior either with ITS or without ITS. As the driving behavior is situation dependent behavior, there is a great advantage from using the driving simulator because various situational (e.g., traffic situation) and environmental factors can be controlled. The driving simulator is composed of a 290 degree screen and a hexapod motion platform with a fully instrumented vehicle cabin. The road environment for the simulator was a town area with a complex road network and various buildings. The traffic control system can control a traffic scenario with 81 vehicles and 72 pedestrians. Since simulator sickness was one of main concerns of using the hi-fidelity driving simulator, we developed the Simplified Simulator Sickness Questionnaire (SSSQ) to evaluate the subjective severity of simulator sickness. The SSSQ consisted of three subscores for nausea, oculomotor, and disorientation symptoms. A preliminary experiment was conducted to investigate the change in severity of the sickness and the mental workload using SSSQ and NASA-TLX when simulator driving was repeated twelve times. We found that the severity of simulator sickness and the workload decreased with repeated simulator drives. When focusing on the change among three simulator drives within one day, the Nausea subscore decreased but the Oculomotor subscore increased.
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Simulator sickness can increase dropout rates and bias participant behavior in driving simulator experiments. It has been suggested that participants experienced with simulator driving are less susceptible to simulator sickness. The authors hypothesized that gaming experience may likewise reduce the susceptibility to simulator sickness. It was also proposed to decrease optic flow in the virtual driving scene to reduce simulator sickness. The preceding assumptions were investigated in a driving simulator experiment with N=26 participants. Results indicate that gamers are less susceptible to simulator sickness compared to non-gamers. Regarding optic flow, only descriptive tendencies could be observed in terms of lower simulator sickness with reduced optic flow. Future studies should be aimed at investigating how gaming experience can be used in order to reduce simulator sickness in driving simulators. The effect of optic flow should be reconsidered in a larger experiment.
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Driving simulator is widely used for examining driver behavior, developing new vehicle safety systems and training for driving license. The external validity of a driving simulator is measured by the comparison of actual speed between simulation and real world. The objective of this study is to validate the speed perception of a typical driving simulator scenario system based on edge rate and optic flow rate, which mainly influence human's speed perception. In a driving simulator scenario system, we optimized the scenario system by adjusting density of road guardrail and density of dashed line. Results from all the experiments showed that each factor influencing speed perception could be optimized to polish up the driving simulator scenario system. The results of this paper could be applied to optimize driving simulator scenario system.
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This paper demonstrates the design of augmented reality (AR) in-car driving simulator system architecture and its experimental design. AR in-car driving simulator is developed in order to explore its effect towards subjects' simulator sickness when operating the driving simulator. Sensory conflict theory postulates that simulator sickness is a condition where an information between vestibular input system and visual that provides orientation and movement information is misaligned. Simulator sickness is a well-known issue entangled with simulator and various past research revealed that some of the methods to avoid simulator sickness are by taking prescribed medicine, shortening the length of drives and adjusting light of driving simulation environment. Augmented reality allows a combination between real world views with computer-generated object and runs in a real-time performance. By using AR, it is possible to create an outdoor driving simulator that allows a real world view with a mixture of case study that been replicate by computer. Affording similar driving experience to driving a normal car, AR is believed to be an alternative solution to simulator sickness. Therefore, the AR driving simulator would be employed in experimental study to measure its effect on simulator sickness towards subjects.
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