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Ergoneers VTK (Vehicle testing kit)

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Total results: 145

Driving simulator development with two degrees of freedom motion for driver behavior study

Year: 2017

Authors: MFM Siam, N Borhan, A Sukardi

This paper discusses about the development of a driving simulator with 2 Degree of Freedom (DOF) motion platform as a data collection tool for driver behavior research. The simulator was installed with motion platform, steering wheel, pedals, transmission, screens, computer, simulation software and sound system to record the driving behaviors in simulated traffic environment. Data containing information such as participants’ response time, vehicle speed, acceleration, braking, turn signals use and vehicle positioning were collected. This paper also discusses the benefits of driving simulator development for driver behavior research while addressing its challenge and limitation for future improvement. The paper concludes that the driving simulator development can contribute significantly to road safety research specifically in driver behavior study.

Simulator
Software

2 versions available

Gaze direction when driving after dark on main and residential roads: Where is the dominant location?

Year: 2017

Authors: J Winter,S Fotios,S Völker

CIE Joint Technical Committee JTC-1 has requested data regarding the size and shape of the distribution of drivers’ eye movement in order to characterize visual adaptation. This paper reports the eye movement of drivers along two routes in Berlin after dark, a main road and a residential street, captured using eye tracking. It was found that viewing behaviour differed between the two types of road. On the main road eye movement was clustered within a circle of approximately 10° diameter, centred at the horizon of the lane. On the residential street eye movement is clustered slightly (3.8°) towards the near side, eye movements were best captured with either an ellipse of approximate axes 10° vertical and 20° horizontal, centred on the lane ahead, or a 10° circle centred 3.8° towards the near side. These distributions reflect a driver’s tendency to look towards locations of anticipated hazards.

Simulator
Software

6 versions available

Hazard Perception Test via Driving Simulator

Year: 2017

Authors: N Borhan,MKA Ibrahim, AA Ab Rashid, MFM Siam

Hazard Perception Test (HPT) is one (1) of longstanding approach in many countries to assess individuals’ competency before obtaining driving licenses. In Malaysia, however, HPT is yet to be part of the national licensing system. Previous hazard perception studies using Malaysian samples reported mixed findings on the effectivity of reaction time-based HPT (e.g. Lim, Sheppard & Crundall, 2013; Ab Rashid & Ibrahim, 2017). Unlike these studies that adopted computer-based HPT, current study employed a full-size cabin driving simulator to study hazard perception between two (2) groups of drivers: novice and experienced. Results from 28 (15 novices, 13 experienced) drivers indicated that novice drivers detected hazards faster than their experienced counterpart, even though both groups have the same performance of hazard recognition. Correlational analysis revealed that driving frequency might be a factor contributing to the difference of response time between these two (2) groups. Further analysis also indicates that different road environments contribute to different hazard perception performance. It is recommended that hazard perception test should be put as a part of the national licensing system and the potential of driving simulator as a HPT tool can be explore more.

Simulator
Software

1 version available:

New motion cueing algorithm for improved evaluation of vehicle dynamics on a driving simulator

Year: 2017

Authors: W Brems, N Kruithof, R Uhlmann, A Wagner

In recent years, driving simulators have become a valuable tool in the automotive design and testing process. Yet, in the field of vehicle dynamics, most decisions are still based on test drives in real cars. One reason for this situation can be found in the fact that many driving simulators do not allow the driver to evaluate the handling qualities of a simulated vehicle. In a driving simulator, the motion cueing algorithm tries to represent the vehicle motion within the constrained motion envelope of the motion platform. By nature, this process leads to so called false cues where the motion of the platform is not in phase or moving in a different direction with respect to the vehicle motion. In a driving simulator with classical filter-based motion cueing, false cues make it considerably more difficult for the driver to rate vehicle dynamics. A team with members from the University of Stuttgart, Cruden B.V., and AUDI AG developed a new motion cueing methodology for the use in a driving simulator dedicated to vehicle dynamics. The new algorithm is a track based approach that makes use of the vehicle’s position on the track and does not use high-pass filters. It therefore allows minimization of false cues, thereby giving the driver the best possible information on the handling qualities of the car. In this paper, the basic principles of the algorithm are described, as well as the implementation in a driving simulator. Comparison with data from a handling track shows the advantages of the new methodology over the classical motion cueing approach.

Simulator
Software

2 versions available

Proceedings of European Conference on Human Centred Design for Intelligent Transport Systems

Year: 2017

Authors: A Morris, L Mendoza

The instrument cluster in the trucks become screens and this brings new challenges for the speedometer design. Both traditional speedometers (i.e. analogue and digital) present design advantages. However, the existing human-factors literature does not allow concluding whether one or the other type is more usable and less distracting. Digital speedometers would be more appropriate for absolute and relative reading, while analogue speedometers would be more efficient and less distracting for detecting dynamic speed change. This study compared both speedometer types on a screen instrument cluster in simulated truck driving. The task-dependant results were replicated. This study updates previous literature and provides a basis to investigate other speedometer types which would be efficient on the three tasks.

Eye Tracking Glasses
Simulator

1 version available:

Ready for take-over? A new driver assistance system for an automated classification of driver take-over readiness

Year: 2017

Authors: C Braunagel,W Rosenstiel

Recent studies analyzing driver behavior report that various factors may influence a driver's take-over readiness when resuming control after an automated driving section. However, there has been little effort made to transfer and integrate these findings into an automated system which classifies the driver's take-over readiness and derives the expected take-over quality. This study now introduces a new advanced driver assistance system to classify the driver's takeover readiness in conditionally automated driving scenarios. The proposed system works preemptively, i.e., the driver is warned in advance if a low take-over readiness is to be expected. The classification of the take-over readiness is based on three information sources: (i) the complexity of the traffic situation, (ii) the current secondary task of the driver, and (iii) the gazes at the road. An evaluation based on a driving simulator study with 81 subjects showed that the proposed system can detect the take-over readiness with an accuracy of 79%. Moreover, the impact of the character of the take-over intervention on the classification result is investigated. Finally, a proof of concept of the novel driver assistance system is provided showing that more than half of the drivers with a low take-over readiness would be warned preemptively with only a 13% false alarm rate.

Simulator
Software

5 versions available

Virtual eye height and display height influence visual distraction measures in simulated driving conditions

Year: 2017

Authors: P Larsson,J Engström, C Wege

Glance behaviour towards in-vehicle visual displays is likely not only a result of the design of the display itself, but also influenced by other factors such as the position of the display and characteristics of the surrounding road scene. In the current study, it was hypothesized that both display position and simulator view will affect a driver’s glance behaviour. A simulator study was conducted in which 25 participants drove in a highway scenario while performing three different tasks in a smartphone positioned at two different heights. Two different simulator views used: one corresponding to the view from the driver’s seat of a truck and the other one corresponded to the view from the driver’s seat of a car. A within-group design was used with simulator view, smartphone position, and task as factors. Results showed that type of view and display position to some extent influenced glance behaviour as well as subjective ratings of driving performance. These results may have implications for eye glance measurement procedures as well as for guidelines relating to driver distraction, e.g. that simulated road scenes must correspond to the vehicle class that the device under test is intended for.

Eye Tracking Glasses
Simulator

2 versions available

Visual distraction effects of in-car text entry methods: Comparing keyboard, handwriting and voice recognition

Year: 2017

Authors: T Kujala,H Grahn

Three text entry methods were compared in a driving simulator study with 17 participants. Ninety-seven drivers' occlusion distance (OD) data mapped on the test routes was used as a baseline to evaluate the methods' visual distraction potential. Only the voice recognition-based text entry tasks passed the set verification criteria. Handwriting tasks were experienced as the most demanding and the voice recognition tasks as the least demanding. An individual in-car glance length preference was found, but against expectations, drivers' ODs did not correlate with in-car glance lengths or visual short-term memory capacity. The handwriting method was further studied with 24 participants with instructions and practice on writing eyes-on-road. The practice did not affect the test results. The findings suggest that handwriting could be visually less demanding than touch screen typing but the reliability of character recognition should be improved or the driver well-experienced with the method to minimize its distraction potential.

Eye Tracking Glasses
Simulator

2 versions available

Assisting drivers with ambient take-over requests in highly automated driving

Year: 2016

Authors: SS Borojeni,L Chuang,W Heuten,S Boll

Take-over situations in highly automated driving occur when drivers have to take over vehicle control due to automation shortcomings. Due to high visual processing demand of the driving task and time limitation of a take-over maneuver, appropriate user interface designs for take-over requests (TOR) are needed. In this paper, we propose applying ambient TORs, which address the peripheral vision of a driver. Conducting an experiment in a driving simulator, we tested a) ambient displays as TORs, b) whether contextual information could be conveyed through ambient TORs, and c) if the presentation pattern (static, moving) of the contextual TORs has an effect on take-over behavior. Results showed that conveying contextual information through ambient displays led to shorter reaction times and longer times to collision without increasing the workload. The presentation pattern however, did not have an effect on take-over performance.

Simulator
Software

6 versions available

Driver Demand: Eye Glance Measures

Year: 2016

Authors: S Seaman, L Hsieh, R Young

This study investigated driver glances while engaging in infotainment tasks in a stationary vehicle while surrogate driving: watching a driving video recorded from a driver’s viewpoint and projected on a large screen, performing a lane-tracking task, and performing the Tactile Detection Response Task (TDRT) to measure attentional effects of secondary tasks on event detection and response. Twenty-four participants were seated in a 2014 Toyota Corolla production vehicle with the navigation system option. They performed the lane-tracking task using the vehicle’s steering wheel, fitted with a laser pointer to indicate wheel movement on the driving video. Participants simultaneously performed the TDRT and a variety of infotainment tasks, including Manual and Mixed-Mode versions of Destination Entry and Cancel, Contact Dialing, Radio Tuning, Radio Preset selection, and other Manual tasks. Participants also completed the 0-and 1-Back pure auditory-vocal tasks. Glances were recorded using an eye-tracker, and validated by manual inspection. Glances were classified as on-road (i.e., looking through the windshield) or off-road (i.e., to locations other than through the windshield). Three off-road glance metrics were tabulated and scored using the NHTSA Guidelines methods: Mean Single Glance Duration (MSGD), Total Eyes-Off-Road Time (TEORT), and Long Glance Proportion (LGP). Comparisons were made for these metric values between the task conditions and a 30-s Baseline condition with no task. Mixed-Mode tasks did not have a statistically significant longer MSGD or TEORT, or higher LGP, than Baseline (except for Mixed-Mode Destination Entry), whereas all the Manual tasks did. Mixed-Mode tasks improved compliance with the NHTSA Guidelines.

Eye Tracking Glasses
Simulator

2 versions available