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Total results: 87

A missing link between fidelity and realism: an experts’ assessment of an advanced motion-based driving simulator

Year: 2021

Authors: M Luzuriaga, S Trunzer

A major concern about advanced motion-based simulators is their level of fidelity i.e., how close the motion sensation in a simulator is to the one perceived in a real vehicle. In this study, we collect the assessment from an exceptional sample composed by n = 33 automotive industry experts who were asked to evaluate the fidelity in terms of steering, braking and speed. Given the subjective nature of our measure, we propose a censored-data Tobit regression model that accounts for this issue, thus providing more accurate estimations. Our results show that, on average, experts evaluated the steering actions close to the maximum level of fidelity. However, braking and speed were evaluated lower in realism, and in fact both diminished the overall fidelity judgement by up to 50%. Moreover, coefficients indicate that steering contributes more to the judgement of fidelity than braking and speed actions. Heterogeneity in the experts' responses and general implications are discussed.

Simulator
Software

7 versions available

Comfort optimization of adaptive cruise control based on heart rate variability and fuzzy control

Year: 2021

Authors: Z Yang, WH Fu, Z Zhang, J Zhang

This paper investigated the impact of braking intensity of self-driving cars at different initial speeds on straight road sections on drivers' comfort, with a view to achieving the comfort optimization of adaptive cruise control (ACC). Specifically, the real vehicle test was conducted in an enclosed venue based on the within-subjects design of 3 × 3 × 2, and the data pertaining to electrocardiogram (ECG) and subjective evaluation of 9 subject drivers in 9 sub tests were collected. Besides, the impacts of different motion states on heart rate variability (HRV) parameters were analyzed using the general linear model for repeated measures, and the relationships among drivers' comfort, decelerations, and standard deviation of NN intervals (SDNN, an index of HRV) were obtained based on subjective and objective analyses. Additionally, a control strategy based on HRV and fuzzy control was formulated to realize the comfort optimization of ACC in case of an abrupt deceleration of the preceding vehicle, the verifications were performed through joint simulation. The results exhibited that the control strategy based on HRV and fuzzy control could shorten the deceleration time in case of an abrupt deceleration of the preceding vehicle, and may improve the comfort in such scenario.

Eye Tracking Glasses
Software

1 version available:

Drivers’ visual characteristics in small-radius optically long tunnels on rural roads

Year: 2021

Authors: S Wang, Z Du, G Chen, H Zheng, Z Tang

This study aims to investigate drivers’ visual characteristics under different radii and turning conditions in small-radius optically long tunnels on rural roads. Fixation and saccade were our main research objectives. We conducted real vehicle tests in optically long tunnels under four different radii. Using the distribution of gaze points, fixation duration, and fixation frequency, the drivers’ fixation characteristics were examined. In addition, the drivers’ saccade characteristics were examined by selecting the saccade duration, saccade frequency, and saccade amplitude. Accordingly, we established mathematical models of fixation duration and saccade duration with a radius under different turning conditions in different zones. Along with the visual task, we further examined drivers’ characteristics in optically long tunnels. We found that the smaller the tunnel radius, the more focused gaze points on inside of the curves, the larger the fixation duration, and the lower the safety with higher psychological pressure. In the zone where the exit portal was invisible, drivers’ tension and risk were higher during turning right, whereas drivers’ tension and risk were higher during turning left in the zone that the exit portal was visible.

Eye Tracking Glasses
Simulator

2 versions available

Drivers’ Visual Characteristics of Urban Expressway Based on Eye Tracker

Year: 2021

Authors: T Feng, Z Zhao, X Tian

In order to compare and analyze the visual characteristics of drivers in the congested and unblocked state of urban expressways, real vehicle tests were carried out on the eastern expressway in Changchun City using the German Dikablis eye tracker and its supportingD-Lab software. The test data was processed by using descriptive statistical analysis and non-parametric inspection methods to quantify the impact of congestion on the driver’s visual characteristics. The results show that drivers mainly obtain traffic information by gaze when driving on the expressway, and the gaze points are mostly concentrated on the road vehicles; the driver’s gaze duration and scan duration in the congested state account for the highest proportions in the 200–250 ms and 0–25 ms time periods, respectively; the average gaze duration and the average scan duration of the drivers in the congested state were higher than those in the unblocked state. The driver's gaze duration and saccade duration in the two states are significantly different, and the Mann–Whitney U test results are less than 0.05; the pupil area changes more drastically in the congested state, and the pupil area change rate is 38.67%.

Eye Tracking Glasses
Software

4 versions available

Multitasking in driving as optimal adaptation under uncertainty

Year: 2021

Authors: JPP Jokinen,T Kujala,A Oulasvirta

Objective: The objective was to better understand how people adapt multitasking behavior when circumstances in driving change and how safe versus unsafe behaviors emerge. Background: Multitasking strategies in driving adapt to changes in the task environment, but the cognitive mechanisms of this adaptation are not well known. Missing is a unifying account to explain the joint contribution of task constraints, goals, cognitive capabilities, and beliefs about the driving environment. Method: We model the driver’s decision to deploy visual attention as a stochastic sequential decision-making problem and propose hierarchical reinforcement learning as a computationally tractable solution to it. The supervisory level deploys attention based on per-task value estimates, which incorporate beliefs about risk. Model simulations are compared against human data collected in a driving simulator. Results: Human data show adaptation to the attentional demands of ongoing tasks, as measured in lane deviation and in-car gaze deployment. The predictions of our model fit the human data on these metrics. Conclusion: Multitasking strategies can be understood as optimal adaptation under uncertainty, wherein the driver adapts to cognitive constraints and the task environment’s uncertainties, aiming to maximize the expected long-term utility. Safe and unsafe behaviors emerge as the driver has to arbitrate between conflicting goals and manage uncertainty about them. Application: Simulations can inform studies of conditions that are likely to give rise to unsafe driving behavior.

Simulator
Software

13 versions available

Novel time-delay side-collision warning model at non-signalized intersections based on vehicle-to-infrastructure communication

Year: 2021

Authors: N Lyu, J Wen, C Wu

In complex traffic environments, collision warning systems that rely only on in-vehicle sensors are limited in accuracy and range. Vehicle-to-infrastructure (V2I) communication systems, however, offer more robust information exchange, and thus, warnings. In this study, V2I was used to analyze side-collision warning models at non-signalized intersections: A novel time-delay side-collision warning model was developed according to the motion compensation principle. This novel time-delay model was compared with and verified against a traditional side-collision warning model. Using a V2I-oriented simulated driving platform, three vehicle-vehicle collision scenarios were designed at non-signalized intersections. Twenty participants were recruited to conduct simulated driving experiments to test and verify the performance of each collision warning model. The results showed that compared with no warning system, both side-collision warning models reduced the proportion of vehicle collisions. In terms of efficacy, the traditional model generated an effective warning in 84.2% of cases, while the novel time-delay model generated an effective warning in 90.2%. In terms of response time and conflict time difference, the traditional model gave a longer response time of 0.91 s (that of the time-delay model is 0.78 s), but the time-delay model reduced the driving risk with a larger conflict time difference. Based on an analysis of driver gaze change post-warning, the statistical results showed that the proportion of effective gaze changes reached 84.3%. Based on subjective evaluations, drivers reported a higher degree of acceptance of the time-delay model. Therefore, the time-delay side-collision warning model for non-signalized intersections proposed herein can improve the applicability and efficacy of warning systems in such complex traffic environments and provide reference for safety applications in V2I systems.

Simulator
Software

12 versions available

The design and integration of a comprehensive measurement system to assess trust in automated driving

Year: 2021

Authors: A Madison, A Arestides, S Harold

With the increased availability of commercially automated vehicles, trust in automation may serve a critical role in the overall system safety, rate of adoption, and user satisfaction. We developed and integrated a novel measurement system to better calibrate human-vehicle trust in driving. The system was designed to collect a comprehensive set of measures based on a validated model of trust focusing on three types: dispositional, learned, and situational. Our system was integrated into a Tesla Model X to assess different automated functions and their effects on trust and performance in real-world driving (e.g., lane changes, parking, and turns). The measurement system collects behavioral, physiological (eye and head movements), and self-report measures of trust using validated instruments. A vehicle telemetry system (Ergoneers Vehicle Testing Kit) uses a suite of sensors for capturing real driving performance data. This off-the-shelf solution is coupled with a custom mobile application for recording driver behaviors, such as engaging/disengaging automation, during on-road driving. Our initial usability evaluations of components of the system revealed that the system is easy to use, and events can be logged quickly and accurately. Our system is thus viable for data collection and can be used to model user trust behaviors in realistic on-road conditions.

Eye Tracking Glasses
Software

2 versions available

The driving experience lab: simulating the automotive future in a trailer

Year: 2021

Authors: C Schartmüller,A Riener

Driving simulators are typically used to evaluate next-generation automotive user interfaces in user studies as they offer a replicable driving setting that also allows studying safety-critical and/or future systems. However, this AutomotiveUI experience research is often limited to university or company campuses and their students and staff. To combat that, we introduced a mobile driving simulator lab in a car trailer. We present features but also limitations of this lab, report on experiences after the first days of operation, and discuss further use cases beyond research. During 7 days of user studies with the trailer at a national garden festival, we conducted trials with more than 70 participants from diverse backgrounds. However, executing studies at public events also has its limitations, e.g., on accepted trial duration and potential for biased responses.

Eye Tracking Glasses
Simulator

3 versions available

Triangulated Investigation of Trust in Automated Driving: Challenges and Solution Approaches for Data Integration

Year: 2021

Authors: TE Kalayci,EG Kalayci,G Lechner, N Neuhuber

In automated driving, an appropriate level of driver trust is essential to improve safety and ensure zero fatalities. Drivers must have a sufficient level of trust to intervene correctly in safety-critical situations: very low levels may lead to either continuous and excessive monitoring of the functions, reducing the attention paid to the environment or switching off these functions, whereas extreme trust in automated driving functions can result in dangerous driving situations because the environment is either insufficiently monitored or not monitored at all. A deeper understanding of trust in automated driving is challenging and requires a triangulated study in which the type of driver, vehicle usage, and environmental data are varied. However, many previous studies were based on a rather limited set of data sources, often relying on qualitative means such as pre-and-post interviews or trust questionnaires to evaluate trust in autonomous driving functions. Although data gathered through empirical research, such as conducting quantitative surveys or qualitative interviews, are simple to store and analyze, the collection and integration of vehicle and sensor data from different data sources usually pose important technical challenges in practice. Hence, a suitable data collection and integration strategy is required to address these challenges. In this context, we propose a general framework for collecting and integrating data from different sources with diverse capabilities and requirements to determine a driver’s trust in automated driving. Our proposed framework facilitates the integration of empirical and measurement data, allowing a triangulated investigation to provide a road map for the automotive industry.

Eye Tracking Glasses
Software

1 version available:

Urgent Cues While Driving: S3D Take-over Requests

Year: 2021

Authors: F Weidner, F Weidner

In SAE level 3 automated vehicles, the human driver still needs to be ready to take over control in case the vehicle encounters a situation outside its operational design domain. This chapter outlines a case study where smart stereoscopic 3D icons act as a take-over notification.

Simulator
Software

1 version available: