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Driving Simulation

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Total results: 274

Training to support appropriate reliance on advanced driver assistance systems

Year: 2023

Authors: CA DeGuzman

This dissertation explores the training techniques designed to support appropriate reliance on Advanced Driver Assistance Systems (ADAS). As these systems become increasingly prevalent in modern vehicles, it is crucial to ensure that drivers learn to use them correctly to enhance safety and efficiency. The research investigates various training methodologies and their effectiveness in promoting proper engagement with ADAS, addressing potential overreliance or misuse. The study aims to provide insights into how training can be optimized to improve driver interaction with these systems, thus contributing to overall road safety.

3 versions available

Transportation Research Interdisciplinary Perspectives

Year: 2023

Authors: J Girgis, M Powell,B Donmez,J Pratt,P Hess

Introduction: Drivers turning at urban intersections pose a high risk to Vulnerable Road Users (VRUs), such as cyclists and pedestrians. In vehicle collisions with VRUs, driver attention misallocation is considered a leading contributor. While previous naturalistic studies have examined driver gaze behaviors at intersections, findings are limited to general gaze directions obtained through video analysis, meaning specific areas to which drivers attend cannot be determined. Method: We present a secondary analysis of an on-road instrumented vehicle dataset collected in 2019 which offers eye-tracking and video data from 26 experienced drivers (13 cyclists and 13 non-cyclists). Three coders jointly examined eye-tracking footage from four right-signalized turns (n = 96) to quantify drivers’ glance distributions to various areas of interest, including those most relevant to VRU safety when drivers turn. Individual temporal glance patterns and general attention allocation trends are presented and described. Results: (1) Relevant pedestrians were the top objects of glance irrespective of signal status, and (2) at red light turns, driver attention was heavily skewed toward leftward traffic. Conclusions: This analysis provides a detailed report of driver glance distributions toward scene-specific areas (as opposed to general directions) at urban intersections and discusses how these patterns may influence VRU safety. Practical applications: This study provides important information regarding the human factors challenges of vehicle-VRU collisions and their prevention.

1 version available:

Tunnel safety: A pilot study investigating drivers’ fixation characteristics when approaching tunnel entrance at different driving speeds

Year: 2023

Authors: L Qin, DS Yang, YN Weng

This study presents the results of a driving experiment study on spatiotemporal characteristics of drivers’ fixation when entering a tunnel portal with different driving speeds. The study was performed during the daytime in a relatively long tunnel. Six experienced drivers were recruited to participate in the driving experiment. Experimental data of pupil area and fixation point position (from 200 m before the tunnel to the tunnel portal) were collected by non-intrusive eye-tracking equipment for three predetermined vehicle speeds (40 km/h, 60 km/h and 80 km/h). Fixation maps (color-coded maps showing distributed data) were created from fixation point position data to quantify visual behaviour changes. The results demonstrated that vehicle speed has a significant impact on pupil area and fixation zones. Fixation area and average pupil area had a significant negative correlation with vehicle speed during the daytime. Moreover, drivers concentrated more on the tunnel entrance portal, front road pavement and car control wheeling. The results revealed that the relationship between pupil area and vehicle speed fitted an exponential function. Limitations and future directions of the study are also discussed.

2 versions available

Vehicle Braking Performance and Saccadic Eye Movement with Different Illuminance Transmission Exposures in Digital Driving Simulation

Year: 2023

Authors: A Ahmad,SA Rosli,AH Chen

During driving, the eye moves as we shift the focus of our eye from one point of interest to another point, known as saccadic eye movement. Although the eye movement is not affected under different illuminance conditions during driving, the movement is involved in the ability to drive. This study investigates the correlation between saccadic eye movement and vehicle braking performance when the illuminance transmission was reduced by introducing a neutral density filter in front of the eyes. This is conducted by exposing four levels of illuminance transmission which are 100%, 50%, 30%, and 15% with driving simulation as braking performance is measured. Based on the baseline data from our preceding saccadic investigation on the same subjects using the Dikablis eye tracker, the braking performance is analyzed together with the eye movement data. Twenty-eight young adults with proper license and driving experience, as well as a good history of systemic, ocular, and binocular vision health, are involved in this study. The driving task is conducted via driving simulation, with the subjects instructed to drive naturally. There is no significant correlation between the number of saccadic eye movements and all investigated vehicle braking performances (speed, time, and length) under reduced illuminance transmissions of 30% and 15% (p>0.05). While our previous investigation reveals that the saccadic eye movement is not affected by different illuminance transmissions when driving, this current study concludes that the vehicle braking performance is not correlated with the saccades while driving under those low illuminance exposures.

1 version available:

Where to gaze during take-over: eye gaze strategy analysis of different situation awareness and hazard perception levels

Year: 2023

Authors: W Ding, Y Murzello,S Cao

While autonomous vehicles are being developed for the future of surface transportation, drivers today still need to be prepared for takeover. The objective of this study is to understand the optimal gaze strategy during the take-over process. First, an affine transfer method was used to link the eye tracking coordinates and pre-defined Aera-of-Interests (AOIs) locations. Then, independent t-tests were applied to analyze the relevance between the gaze strategy determined by the gaze time percentages on various AOIs and the Situation Awareness (SA) and Hazard Perception (HP) levels. The results showed that drivers with higher SA used different gaze strategies before and after they detected the hazards, while drivers with higher HP kept focusing on the center of the road. Explanations and implications of take-over request design are discussed.

4 versions available

A multimodal psychological, physiological and behavioural dataset for human emotions in driving tasks

Year: 2022

Authors: W Li, R Tan,Y Xing,G Li,S Li, G Zeng, P Wang

Human emotions are integral to daily tasks, and driving is now a typical daily task. Creating a multi-modal human emotion dataset in driving tasks is an essential step in human emotion studies. we conducted three experiments to collect multimodal psychological, physiological and behavioural dataset for human emotions (PPB-Emo). In Experiment I, 27 participants were recruited, the in-depth interview method was employed to explore the driver’s viewpoints on driving scenarios that induce different emotions. For Experiment II, 409 participants were recruited, a questionnaire survey was conducted to obtain driving scenarios information that induces human drivers to produce specific emotions, and the results were used as the basis for selecting video-audio stimulus materials. In Experiment III, 40 participants were recruited, and the psychological data and physiological data, as well as their behavioural data were collected of all participants in 280 times driving tasks. The PPB-Emo dataset will largely support the analysis of human emotion in driving tasks. Moreover, The PPB-Emo dataset will also benefit human emotion research in other daily tasks.

10 versions available

Adaptive Driving Assistant Model (ADAM) for advising drivers of autonomous vehicles

Year: 2022

Authors: SJ Hsieh, AR Wang,A Madison,C Tossell

Fully autonomous driving is on the horizon; vehicles with advanced driver assistance systems (ADAS) such as Tesla's Autopilot are already available to consumers. However, all currently available ADAS applications require a human driver to be alert and ready to take control if needed. Partially automated driving introduces new complexities to human interactions with cars and can even increase collision risk. A better understanding of drivers’ trust in automation may help reduce these complexities. Much of the existing research on trust in ADAS has relied on use of surveys and physiological measures to assess trust and has been conducted using driving simulators. There have been relatively few studies that use telemetry data from real automated vehicles to assess trust in ADAS. In addition, although some ADAS technologies provide alerts when, for example, drivers’ hands are not on the steering wheel, these systems are not personalized to individual drivers. Needed are adaptive technologies that can help drivers of autonomous vehicles avoid crashes based on multiple real-time data streams. In this paper, we propose an architecture for adaptive autonomous driving assistance. Two layers of multiple sensory fusion models are developed to provide appropriate voice reminders to increase driving safety based on predicted driving status. Results suggest that human trust in automation can be quantified and predicted with 80% accuracy based on vehicle data, and that adaptive speech-based advice can be provided to drivers with 90 to 95% accuracy. With more data, these models can be used to evaluate trust in driving assistance tools, which can ultimately lead to safer and appropriate use of these features.

2 versions available

Age Differences in the Situation Awareness and Takeover Performance in a Semi-Autonomous Vehicle Simulator

Year: 2022

Authors: Y Murzello

Research on young and elderly drivers indicates a high crash risk amongst these drivers in comparison to other age groups of drivers. Young drivers have a greater propensity to adopt a risky driving style and behaviors associated with poor road safety. On the other hand, age-related declines can negatively impact the performance of older drivers on the road leading to crashes and risky maneuvers. Thus, autonomous vehicles have been suggested to improve the road safety and mobility of younger and older drivers. However, the difficulty of manually taking over control from semi-autonomous vehicles might vary in different driving conditions, particularly in those that are more challenging. Hence, the present study aims to examine the effect of road geometry and scenario, by investigating young, middle-aged and older drivers’ situation awareness (SA) and takeover performance when driving a semi-autonomous vehicle simulator on a straight versus a curved road on a highway and an urban non-highway road when engaged in a secondary distracting task. Due to the impact of COVID-19, data from only the young (n=24) and middle-aged (n=24) adults were collected and analyzed. Participants drove a Level 3 semi-autonomous simulator vehicle and performed a secondary non-driving related task in the distracted conditions. The results indicated that the participants had significantly longer hazard perception times on the curved roads and autopilot drives, but there was no significant effect of driver age and road type. Their Situation Awareness Global Assessment Technique (SAGAT) scores were higher in the highway scenarios, on the straight roads, and in the manual drive compared to the autopilot with distraction drive. Young drivers were also found to have significantly higher SAGAT scores than middle-aged drivers. While there was a significant interaction effect between road type and road geometry on takeover time, there was no significant main effect of road geometry, drive type and driver’s age. For the takeover quality metrics, road geometry and drive type had an effect on takeover performance. The resulting acceleration was higher for the straight road and in the autopilot drives, and the lane deviation was higher on the curved road and autopilot only drive compared to the autopilot with distraction drive. There was no significant main effect of road type and driver’s age on resulting acceleration and lane deviation. Overall, while there were age differences in some aspects of SA, young and middle-aged drivers did not differ in their takeover performance. The participants’ SA was impacted by the road type and geometry and their takeover quality varied according to the road geometry and drive type. The outcomes of this research will aid vehicle manufacturing companies that are developing Level 3 semi-autonomous vehicles with appropriately designing the lead time of the takeover request to meet the driving style and abilities of younger and middle-aged drivers. This will also help to improve road safety by reducing the crash rate of younger drivers.

1 version available:

Analyzing the influencing factors and workload variation of takeover behavior in semi-autonomous vehicles

Year: 2022

Authors: H Zhang, Y Zhang, Y Xiao, C Wu

There are many factors that will influence the workload of drivers during autonomous driving. To examine the correlation between different factors and the workload of drivers, the influence of different factors on the workload variations is investigated from subjective and objective viewpoints. Thirty-seven drivers were recruited to participant the semi-autonomous driving experiments, and the drivers were required to complete different NDRTs (Non-Driving-Related Tasks): mistake finding, chatting, texting, and monitoring when the vehicle is in autonomous mode. Then, we introduced collision warning to signal there is risk ahead, and the warning signal was triggered at different TB (Time Budget)s before the risk, at which time the driver had to take over the driving task. During driving, the NASA-TLX-scale data were obtained to analyze the variation of the driver’s subjective workload. The driver’s pupil-diameter data acquired by the eye tracker from 100 s before the TOR (Take-Over Request) to 19 s after the takeover were obtained as well. The sliding time window was set to process the pupil-diameter data, and the 119-s normalized average pupil-diameter data under different NDRTs were fitted and modeled to analyze the variation of the driver’s objective workload. The results show that the total subjective workload score under the influence of different factors is as follows: obstacle-avoidance scene > lane-keeping scene; TB = 7 s and TB = 3 s have no significant difference; and mistake finding > chatting > texting > monitoring. The results of pupil-diameter data under different factors are as follows: obstacle-avoidance scene > lane-keeping scene; TB = 7 s > TB = 3 s; and monitoring type (chatting and monitoring) > texting type (mistake finding and texting). The research results can provide a reference for takeover safety prediction modeling based on workload.

13 versions available

Assessing the impact of driver advisory systems on train driver workload, attention allocation and safety performance

Year: 2022

Authors: VJMP Verstappen, EN Pikaar, RGD Zon

Netherlands Railways has developed driver advisory systems (DAS) to provide the train driver with route context information and coasting advice in order to benefit punctuality and energy efficiency. However, the impact of these DAS on human factors aspects and safety performance is unclear. The current study assesses the impact of two DAS levels (route context information and coasting advice) on mental workload, attention allocation and safety performance, using eye tracking, a subjective mental workload rating scale (RSME) and simulator data. The overall findings suggest that the application of DAS levels has no negative impact on safety performance and attention allocation towards the trackside compared to a control condition with static timetable information. Furthermore, safety performance benefits significantly from DAS with route context information. DAS were originally developed to benefit punctuality and energy efficiency goals. This study implicates that DAS can also benefit safety performance. The current study found that DAS could decrease workload when the functionalities meet the requirements of the situation. The possible presence of mental underload and its effect on driving performance should be taken into consideration when implementing DAS. It is essential in the development of DAS that it meaningfully enriches the train driving task in stead of simply increasing mental workload.

7 versions available