scholarly journals Analysis of a Human-Machine Interface for Cooperative Truck Overtaking Maneuvers on Freeways: Increase Success Rate and Assess Driving Behavior during System Failures

2021 ◽  
Vol 5 (11) ◽  
pp. 69
Author(s):  
Jana Fank ◽  
Christian Knies ◽  
Frank Diermeyer
Keyword(s):  
Success Rate ◽  
Driving Simulator ◽  
Road Traffic ◽  
Human Machine Interface ◽  
Truck Drivers ◽  
System Failure ◽  
System Failures ◽  
Road Users ◽  
Potential System ◽  
Machine Interface

Cooperation between road users based on V2X communication has the potential to make road traffic safer and more efficient. The exchange of information enables the cooperative orchestration of critical traffic situations, such as truck overtaking maneuvers on freeways. With the benefit of such a system, questions arise concerning system failure or the abrupt and unexpected behavior of road users. A human-machine interface (HMI) organizes and negotiates the cooperation between drivers and maintains smooth interaction, trust, and system acceptance, even in the case of a possible system failure. A study was conducted with 30 truck drivers on a dynamic truck driving simulator to analyze the negotiation of cooperation requests and the reaction of truck drivers to potential system failures. The results show that an automated cooperation request does not translate into a significantly higher cooperation success rate. System failures in cooperative truck passing maneuvers are not considered critical by truck drivers in this simulated environment. The next step in the development process is to investigate how the success rate of truck overtaking maneuvers on freeways can be further increased as well as the implementation of the system in a real vehicle to investigate the reaction behavior of truck drivers in case of system failures in a real environment.

Distraction of Connected Vehicle Human–Machine Interface for Truck Drivers

2020 ◽  
Vol 2674 (9) ◽  
pp. 438-449 ◽  
Author(s):  
Guangchuan Yang ◽  
Mohamed M. Ahmed ◽  
Biraj Subedi
Keyword(s):  
Human Error ◽  
Driving Simulator ◽  
Situational Awareness ◽  
Revealed Preference ◽  
Human Machine Interface ◽  
Truck Drivers ◽  
Connected Vehicle ◽  
Cognitive Distraction ◽  
High Workload ◽  
Machine Interface

Connected vehicle (CV) technology aims to improve drivers’ situational awareness through audible and visual warnings, commonly displayed on a human–machine interface (HMI), thus reducing the likelihood of crashes caused by human error. Nevertheless, the presence of an in-vehicle CV HMI may pose an increasing threat to driver distraction, particularly for truck drivers and under high workload driving conditions. With this concern, this research investigated the effects of a HMI developed by the Wyoming Department of Transportation CV Pilot on truck drivers’ cognitive distraction and driving behavior through a driving simulator experiment. Revealed preference survey and vehicle dynamics data were employed to assess the cognitive distractions of the Pilot’s HMI. Simulation results indicated that when CV warnings were displayed on the HMI, they did not introduce significant effects on participants’ longitudinal and lateral control of the vehicle. Nevertheless, from the revealed preference survey, it was found that approximately 27% of the participants indicated that the CV HMI tended to introduce additional visual workload for them, particularly when approaching an active freeway work zone under reduced visibility condition. In this regard, this research pointed out that the design of CV warnings and HMI displays needs to incorporate drivers’ ability to recognize and react safely to the received CV warnings to minimize the cognitive distractions introduced by the CV HMI.

scholarly journals Evaluation of Multimodal External Human–Machine Interface for Driverless Vehicles in Virtual Reality

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 687
Author(s):  
Jinzhen Dou ◽  
Shanguang Chen ◽  
Zhi Tang ◽  
Chang Xu ◽  
Chengqi Xue
Keyword(s):  
New Technology ◽  
Pedestrian Safety ◽  
Human Machine Interface ◽  
Visual Features ◽  
Visual Modality ◽  
Human Voice ◽  
Road Users ◽  
Machine Interface ◽  
Interaction Efficiency ◽  
Physical Features

With the development and promotion of driverless technology, researchers are focusing on designing varied types of external interfaces to induce trust in road users towards this new technology. In this paper, we investigated the effectiveness of a multimodal external human–machine interface (eHMI) for driverless vehicles in virtual environment, focusing on a two-way road scenario. Three phases of identifying, decelerating, and parking were taken into account in the driverless vehicles to pedestrian interaction process. Twelve eHMIs are proposed, which consist of three visual features (smile, arrow and none), three audible features (human voice, warning sound and none) and two physical features (yielding and not yielding). We conducted a study to gain a more efficient and safer eHMI for driverless vehicles when they interact with pedestrians. Based on study outcomes, in the case of yielding, the interaction efficiency and pedestrian safety in multimodal eHMI design was satisfactory compared to the single-modal system. The visual modality in the eHMI of driverless vehicles has the greatest impact on pedestrian safety. In addition, the “arrow” was more intuitive to identify than the “smile” in terms of visual modality.

scholarly journals Driving Simulator Validation for In-Vehicle Human Machine Interface Assessment

2019 ◽  
Vol 63 (1) ◽  
pp. 2104-2108
Author(s):  
Thomas McWilliams ◽  
Bruce Mehler ◽  
Bobbie Seppelt ◽  
Bryan Reimer
Keyword(s):  
Behavior Analysis ◽  
Real World ◽  
Driving Simulator ◽  
Human Machine Interface ◽  
Production Level ◽  
Driving Simulators ◽  
Relative Validity ◽  
Ongoing Process ◽  
Human Machine Interfaces ◽  
Machine Interface

Driving simulator validation is an important and ongoing process. Advances in in-vehicle human machine interfaces (HMI) mean there is a continuing need to reevaluate the validity of use cases of driving simulators relative to real world driving. Along with this, our tools for evaluating driver demand are evolving, and these approaches and measures must also be considered in evaluating the validity of a driving simulator for particular purposes. We compare driver glance behavior during HMI interactions with a production level multi-modal infotainment system on-road and in a driving simulator. In glance behavior analysis using traditional glance metrics, as well as a contemporary modified AttenD measure, we see evidence for strong relative validity and instances of absolute validity of the simulator compared to on-road driving.

scholarly journals Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2405
Author(s):  
Heung-Gu Lee ◽  
Dong-Hyun Kang ◽  
Deok-Hwan Kim
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Response Speed ◽  
Autonomous Driving ◽  
Human Machine Interface ◽  
Driver Assistance Systems ◽  
Human Machine Interaction ◽  
Driving Assistance ◽  
Assistant Systems ◽  
Machine Interface

Currently, the existing vehicle-centric semi-autonomous driving modules do not consider the driver’s situation and emotions. In an autonomous driving environment, when changing to manual driving, human–machine interface and advanced driver assistance systems (ADAS) are essential to assist vehicle driving. This study proposes a human–machine interface that considers the driver’s situation and emotions to enhance the ADAS. A 1D convolutional neural network model based on multimodal bio-signals is used and applied to control semi-autonomous vehicles. The possibility of semi-autonomous driving is confirmed by classifying four driving scenarios and controlling the speed of the vehicle. In the experiment, by using a driving simulator and hardware-in-the-loop simulation equipment, we confirm that the response speed of the driving assistance system is 351.75 ms and the system recognizes four scenarios and eight emotions through bio-signal data.

scholarly journals DIFFERENCES IN TRAFFIC CLIMATE BETWEEN DRIVING SIMULATION SCENARIOS WITH DIFFERENT COMPLEXITY LEVELS

2020 ◽  
Vol 8 (17) ◽  
Keyword(s):  
Traffic Accidents ◽  
Driving Simulator ◽  
Road Traffic ◽  
Low Complexity ◽  
Self Report ◽  
Driving Simulation ◽  
Country Level ◽  
Road Users ◽  
Level Measurement ◽  
Climate Scale

Road traffic injuries are one of the most important public health problems all over the world. Despite of the globality of the problem, driver behaviors, road traffic accidents and injuries show regional difference. Perceived traffic climate is related to driver behaviors. In order to predict driver behaviors, Traffic Climate Scale is used, which measures road users’ perceptions towards traffic system. Previously, the validity of Traffic Climate Scale was tested with self-report measures (i.e. Driver Behavior Questionnaire) and with simulator based results. Characteristics of simulator scenarios show differences based on purpose of research questions. However, researchers do not have enough information about whether participants perceive these differences or not. With respect to this, the aim of the present study is to test whether Traffic Climate Scale could be used to evaluate the characteristics of a simulated driving environment. For this reason, a total of 78 participants between the ages of 18 and 25 (M = 22.28, SD = 1.64) drove two driving simulation scenarios. High complexity scenario was perceived as more internally demanding than low complexity scenario. The results showed that, in addition to the country-level measurement of traffic climate, traffic climate measurement might be used to evaluate the perception of driving simulation scenarios. There is also a need of future studies that includes different driving simulators and scenarios. Keywords Internal requirements, traffic climate, driving simulator, road safety

Effects of Human–Machine Interface Design for Intelligent Speed Adaptation on Driving Behavior and Acceptance

2005 ◽  
Vol 1937 (1) ◽  
pp. 79-86
Author(s):  
Arno M. Rook ◽  
Jeroen H. Hogema
Keyword(s):  
Interface Design ◽  
Driving Simulator ◽  
Driving Behavior ◽  
Human Machine Interface ◽  
Speed Limit ◽  
Compensatory Behavior ◽  
Driving Speed ◽  
Moving Base ◽  
Machine Interface ◽  
Intelligent Speed Adaptation

The effects of human–machine interface (HMI) design for intelligent speed adaptation (ISA) on driving behavior and acceptance were measured in a moving-base research driving simulator. Sixty-four experienced drivers participated in two simulator experiments (32 in each). During the simulated runs with ISA, the speed limit was communicated through the ISA system. The ISA system consisted of an indication of the speed limit on the speedometer and a gas pedal that could be used either as a haptic or tactile pedal or as a dead throttle. Two versions of the haptic gas pedal were examined in Experiment I: a low-force ISA (easy to overrule, informative in nature) and a high-force ISA (stronger counterforce, more compulsory in nature). Two other configurations were tested in Experiment II: a tactile pedal (a vibration on the gas pedal, informative in nature) and a dead throttle (completely restraining the driver from exceeding the speed limit). It was hypothesized that the closer the ISA is to an informative type, the higher the acceptance and the smaller the effects on driving behavior would be. This hypothesis appeared to be valid, although for both driving behavior and acceptance, not all four HMIs could be ranked unambiguously on the scale from no ISA to full ISA. In sharp curves, drivers appeared to choose a driving speed below the speed limit, irrespective of ISA. The specific road environment scenarios that were inserted to examine presupposed compensatory behavior for experienced delay indicated no signs of compensatory driving behavior.

scholarly journals How Much Space Is Required? Effect of Distance, Content, and Color on External Human–Machine Interface Size

Information ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 346 ◽  
Author(s):  
Michael Rettenmaier ◽  
Jonas Schulze ◽  
Klaus Bengler
Keyword(s):  
Repeated Measures ◽  
Human Machine Interface ◽  
Human Detection ◽  
Human Interaction ◽  
Minimum Size ◽  
Detection Range ◽  
Content Type ◽  
Repeated Measures Design ◽  
Road Users ◽  
Machine Interface

The communication of an automated vehicle (AV) with human road users can be realized by means of an external human–machine interface (eHMI), such as displays mounted on the AV’s surface. For this purpose, the amount of time needed for a human interaction partner to perceive the AV’s message and to act accordingly has to be taken into account. Any message displayed by an AV must satisfy minimum size requirements based on the dynamics of the road traffic and the time required by the human. This paper examines the size requirements of displayed text or symbols for ensuring the legibility of a message. Based on the limitations of available package space in current vehicle models and the ergonomic requirements of the interface design, an eHMI prototype was developed. A study involving 30 participants varied the content type (text and symbols) and content color (white, red, green) in a repeated measures design. We investigated the influence of content type on content size to ensure legibility from a constant distance. We also analyzed the influence of content type and content color on the human detection range. The results show that, at a fixed distance, text has to be larger than symbols in order to maintain legibility. Moreover, symbols can be discerned from a greater distance than text. Color had no content overlapping effect on the human detection range. In order to ensure the maximum possible detection range among human road users, an AV should display symbols rather than text. Additionally, the symbols could be color-coded for better message comprehension without affecting the human detection range.

scholarly journals Context-Adaptive Availability Notifications for an SAE Level 3 Automation

2021 ◽  
Vol 5 (4) ◽  
pp. 16
Author(s):  
Simon Danner ◽  
Alexander Feierle ◽  
Carina Manger ◽  
Klaus Bengler
Keyword(s):  
Driving Simulator ◽  
Human Machine Interface ◽  
Gaze Behavior ◽  
Activation Time ◽  
Automated Driving ◽  
Driving Function ◽  
The Road ◽  
Level 3 ◽  
Machine Interface ◽  
Simulator Study

Context-adaptive functions are not new in the driving context, but even so, investigations into these functions concerning the automation human–machine interface (aHMI) have yet to be carried out. This study presents research into context-adaptive availability notifications for an SAE Level 3 automation in scenarios where participants were surprised by either availability or non-availability. For this purpose, participants (N = 30) took part in a driving simulator study, experiencing a baseline HMI concept as a comparison, and a context-adaptive HMI concept that provided context-adaptive availability notifications with the aim of improving acceptance and usability, while decreasing frustration (due to unexpected non-availability) and gaze deviation from the road when driving manually. Furthermore, it was hypothesized that participants, when experiencing the context-adaptive HMI, would activate the automated driving function more quickly when facing unexpected availability. None of the hypotheses could be statistically confirmed; indeed, where gaze behavior was concerned, the opposite effects were found, indicating increased distraction induced by the context-adaptive HMI. However, the trend in respect to the activation time was towards shorter times with the context-adaptive notifications. These results led to the conclusion that context-adaptive availability notifications might not always be beneficial for users, while more salient availability notifications in the case of an unexpected availability could be advantageous.

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