Статті в журналах: "Vehicle-driver interaction"

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Dargahi Nobari, Khazar, Franz Albers, Katharina Bartsch, Jan Braun, and Torsten Bertram. "Modeling driver-vehicle interaction in automated driving." Forschung im Ingenieurwesen 86, no. 1 (January 24, 2022): 65–79. http://dx.doi.org/10.1007/s10010-021-00576-6.

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AbstractIn automated vehicles, the collaboration of human drivers and automated systems plays a decisive role in road safety, driver comfort, and acceptance of automated vehicles. A successful interaction requires a precise interpretation and investigation of all influencing factors such as driver state, system state, and surroundings (e.g., traffic, weather). This contribution discusses the detailed structure of the driver-vehicle interaction, which takes into account the driving situation and the driver state to improve driver performance. The interaction rules are derived from a controller that is fed by the driver state within a loop. The regulation of the driver state continues until the target state is reached or the criticality of the situation is resolved. In addition, a driver model is proposed that represents the driver’s decision-making process during the interaction between driver and vehicle and during the transition of driving tasks. The model includes the sensory perception process, decision-making, and motor response. The decision-making process during the interaction deals with the cognitive and emotional states of the driver. Based on the proposed driver-vehicle interaction loop and the driver model, an experiment with 38 participants is performed in a driving simulator to investigate (1) if both emotional and cognitive states become active during the decision-making process and (2) what the temporal sequence of the processes is. Finally, the evidence gathered from the experiment is analyzed. The results are consistent with the suggested driver model in terms of the cognitive and emotional state of the driver during the mode change from automated system to the human driver.

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2

Krupenia, Stas. "Book Review: Automotive Ergonomics: Driver–Vehicle Interaction." Ergonomics in Design: The Quarterly of Human Factors Applications 23, no. 4 (October 2015): 29. http://dx.doi.org/10.1177/1064804615613314.

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3

Perterer, Nicole, Susanne Stadler, Alexander Meschtscherjakov, and Manfred Tscheligi. "Driving Together Across Vehicle." International Journal of Mobile Human Computer Interaction 11, no. 2 (April 2019): 58–74. http://dx.doi.org/10.4018/ijmhci.2019040104.

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Most research on vehicle-to-vehicle (V2V) communication is technology-driven, or focused on driver-to-driver interaction. Social communication between drivers and passengers across vehicles, with the same destination, is often neglected. Communication is influenced by context and occupant behavior, and has a significant effect on the collaborative driving scenario. An exploratory in-situ study with seven groups of two driver/co-driver pairs each, located in two separate vehicles, was conducted. On a predefined route, different subtasks had to be solved in a collaborative way. The study revealed a significant influence of different social factors, such as driving behavior, and contextual factors such as weather conditions, or vehicle shape and size. Findings delivered important insights and a deeper understanding on collaborative driving that may influence future V2V communication technologies. Additionally, the collaborative driving behavior of the driver/co-driver pairs could be transferred to a multi-agent framework.

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PODOPRIGORA, N. V., and P. A. PEGIN. "SYSTEM APPROACH IN INFORMATION SUPPORT OF THE «ROAD USER-VEHICLE-ROAD-EXTERNAL ENVIRONMENT»." World of transport and technological machines 78, no. 3-5 (2022): 73–77. http://dx.doi.org/10.33979/2073-7432-2022-5(78)-3-73-77.

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The article is devoted to the study of aspects of information interaction of the driver subsystem while driving a vehicle. The purpose of writing the paper is to publish the unified information model of interaction of the «Driver» subsystem with other subsystems of the classical «Driver-Car-Road-Environment» system proposed by the author. In the article the author analyzes the in-formation signals coming to the driver from the controlled vehicle, the road, the environment, in-cluding other participants of the road. The author builds channels of information interaction be-tween individual subsystems «Driver - Vehicle - Road – Environment». The proposed changes the author presents in the form of a unified holistic information system «Road user-Vehicle-Road-External Environment».

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5

Ozsoy, Burak, Xuewu Ji, James Yang, Jared Gragg, and Bradley Howard. "Simulated effect of driver and vehicle interaction on vehicle interior layout." International Journal of Industrial Ergonomics 49 (September 2015): 11–20. http://dx.doi.org/10.1016/j.ergon.2015.05.004.

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PODOPRIGORA, N. V. "SYSTEM APPROACH IN INFORMATION SUPPORT OF THE «ROAD USER-VEHICLE-ROAD-EXTERNAL ENVIRONMENT»." World of transport and technological machines 77, no. 2 (2022): 70–75. http://dx.doi.org/10.33979/2073-7432-2022-76-1-70-75.

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The article is devoted to the study of aspects of information interaction of the driver subsystem while driving a vehicle. The purpose of writing the paper is to publish the unified information model of interaction of the «Driver» subsystem with other subsystems of the classical «Driver-Car-Road-Environment» system proposed by the author. In the article the author analyzes the infor-mation signals coming to the driver from the controlled vehicle, the road, the environment, includ-ing other participants of the road. The author builds channels of information interaction between individual subsystems «Driver - Vehicle - Road – Environment». The proposed changes the author presents in the form of a unified holistic information system «Road user-Vehicle-Road-External Environment».The scientific novelty lies in the possibility to carry out the analysis and modeling of the in-formation exchange between the driver and other subsystems «road user - vehicle - road – envi-ronment» in order to prevent and predict road accidents.

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7

Graichen, Lisa, Matthias Graichen, and Josef F. Krems. "Evaluation of Gesture-Based In-Vehicle Interaction: User Experience and the Potential to Reduce Driver Distraction." Human Factors: The Journal of the Human Factors and Ergonomics Society 61, no. 5 (January 29, 2019): 774–92. http://dx.doi.org/10.1177/0018720818824253.

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Objective: We observe the effects of in-vehicle system gesture-based interaction versus touch-based interaction on driver distraction and user experience. Background: Driver distraction is a major problem for traffic safety, as it is a contributing factor to a number of accidents. Visual distraction in particular has a highly negative impact on the driver. One possibility for reducing visual driver distraction is to use new forms of interaction in the vehicle, such as gesture-based interaction. Method: In this experiment, participants drove on a motorway or in a city scenario while using touch-based interaction or gesture-based interaction. Subjective data, such as acceptance and workload, and objective data, including glance behavior, were gathered. Results: As a result, participants rated their subjective impressions of safe driving as higher when using gesture-based interaction. More specifically, acceptance and attractiveness were higher, and workload was lower. The participants performed significantly fewer glances to the display and the glances were much shorter. Conclusion: Gestures are a positive alternative for in-vehicle interaction since effects on driver distraction are less significant when compared to touch-based interaction. Application: Potential application of this research includes interaction design of typical in-vehicle information and entertainment functions.

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8

Hanowski, Richard J., Robert J. Carroll, Walter W. Wierwille, and Rebecca L. Olson. "Light Vehicle-Heavy Vehicle Interactions: A Preliminary Assessment Using Critical Incident Analysis." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 22 (September 2002): 1844–47. http://dx.doi.org/10.1177/154193120204602214.

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Two recently completed on-road in situ data collection efforts, one involving local/short haul trucking and the other long-haul trucking, provided a large data set in which to conduct an examination of critical incidents (crashes and near-crashes) that occurred between light vehicles and heavy vehicles. Video and non-video data collected during the two studies were used to characterize critical incidents that were recorded between light vehicle and heavy vehicle drivers. Across both studies, 210 light vehicle-heavy vehicle (LV-HV) critical incidents were recorded. Of these, 78 percent were initiated by the light vehicle driver. Aggressive driving on the part of the light vehicle driver was found to be the primary contributing factor for light vehicle driver initiated incidents. For heavy vehicle driver initiated incidents, the primary contributing factor was poor driving technique. The results suggest that efforts at addressing LV-HV interaction incidents should focus on light vehicle drivers who drive aggressively. Additionally, it is recommended that heavy vehicle drivers might benefit from improved driver training that includes instruction on defensive driving.

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9

Stevens, A. "Safety of driver interaction with in-vehicle information systems." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 214, no. 6 (June 2000): 639–44. http://dx.doi.org/10.1243/0954407001527501.

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10

Damiani, Sergio, Enrica Deregibus, and Luisa Andreone. "Driver-vehicle interfaces and interaction: where are they going?" European Transport Research Review 1, no. 2 (May 13, 2009): 87–96. http://dx.doi.org/10.1007/s12544-009-0009-2.

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11

Orlovska, J., C. Wickman, and R. Soderberg. "THE USE OF VEHICLE DATA IN ADAS DEVELOPMENT, VERIFICATION AND FOLLOW-UP ON THE SYSTEM." Proceedings of the Design Society: DESIGN Conference 1 (May 2020): 2551–60. http://dx.doi.org/10.1017/dsd.2020.322.

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AbstractAdvanced Driver Assistance Systems (ADAS) require a high level of interaction between the driver and the system, depending on driving context at a particular moment. Context-aware ADAS evaluation based on vehicle data is the most prominent way to assess the complexity of ADAS interactions. In this study, we conducted interviews with the ADAS development team at Volvo Cars to understand the role of vehicle data in the ADAS development and evaluation. The interviews’ analysis reveals strategies for improvement of current practices for vehicle data-driven ADAS evaluation.

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12

Takahashi, Hiroshi. "A Study on Designing Vehicle Controllers Using Driver-Vehicle-Driving Environment Interaction Model." Journal of the Robotics Society of Japan 16, no. 5 (1998): 672–83. http://dx.doi.org/10.7210/jrsj.16.672.

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13

Takahashi, Hiroshi, and Kouichi Kuroda. "Study on Intelligent Vehicle Control Considering Driver Perception of Driving Environment." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 1 (February 20, 1999): 42–49. http://dx.doi.org/10.20965/jaciii.1999.p0042.

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We propose an approach to designing an intelligent vehicle controller for partially supporting driver operation of a vehicle. Driving is thought of as a system formed by the interaction between the driving environment, vehicle action, and driver expectations of the vehicle. Driver expectations during driving receive great attention. Driver intent to accelerate or decelerate is mainly generated by the perception of the driving environment. The model we propose involves information on the driving environment affecting driver intent taking driver differences in perceiving the driving environment into account. The model is represented affordance implicitly. An engineering model for installing the vehicle controller is expressed by a multipurpose decision maker allowing explicit treatment of the driving environment, vehicle action, and driver intent. A reasoning engine deals with differences in individual driver traits for generating intent to decelerate by using fuzzy integrals and measures. For example, a control procedure designed with the proposed model is applied to power train control. Simulation and experimental results show good performance by the vehicle control system.

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14

Ataya, Aya, Won Kim, Ahmed Elsharkawy, and SeungJun Kim. "How to Interact with a Fully Autonomous Vehicle: Naturalistic Ways for Drivers to Intervene in the Vehicle System While Performing Non-Driving Related Tasks." Sensors 21, no. 6 (March 21, 2021): 2206. http://dx.doi.org/10.3390/s21062206.

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Autonomous vehicle technology increasingly allows drivers to turn their primary attention to secondary tasks (e.g., eating or working). This dramatic behavior change thus requires new input modalities to support driver–vehicle interaction, which must match the driver’s in-vehicle activities and the interaction situation. Prior studies that addressed this question did not consider how acceptance for inputs was affected by the physical and cognitive levels experienced by drivers engaged in Non-driving Related Tasks (NDRTs) or how their acceptance varies according to the interaction situation. This study investigates naturalistic interactions with a fully autonomous vehicle system in different intervention scenarios while drivers perform NDRTs. We presented an online methodology to 360 participants showing four NDRTs with different physical and cognitive engagement levels, and tested the six most common intervention scenarios (24 cases). Participants evaluated our proposed seven natural input interactions for each case: touch, voice, hand gesture, and their combinations. Results show that NDRTs influence the driver’s input interaction more than intervention scenario categories. In contrast, variation of physical load has more influence on input selection than variation of cognitive load. We also present a decision-making model of driver preferences to determine the most natural inputs and help User Experience designers better meet drivers’ needs.

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15

Li, Shaohua, and Jianying Ren. "Investigation on three-directional dynamic interaction between a heavy-duty vehicle and a curved bridge." Advances in Structural Engineering 21, no. 5 (September 18, 2017): 721–38. http://dx.doi.org/10.1177/1369433217729516.

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Considering the nonlinear property of suspension damping and tire stiffness, a full-vehicle model is built for a heavy-duty truck. A modified preview driver model with nonlinear time delay is inserted into the vehicle model to compute the suitable steering angle of the front wheel and to make the vehicle follow the required route. Next, the finite element model of a five-span continuous curved highway bridge is established, and the bridge’s inherent frequencies and modes are obtained. The curved bridge and the vehicle are coupled by three-directional tire forces, and a three-directional driver–vehicle–bridge interaction model is presented. The presented vehicle model and bridge model are verified by comparing with the published works. The dynamic impact factors of vertical, lateral, and torsional displacements of the bridge are calculated when a vehicle is traversing through the bridge, and the impact factors’ distributions along the bridge are analyzed. The effects of vehicle driving conditions on impact factors are also researched. It is found that the impact factor calculated from the present specification for a straight bridge is smaller than that from the three-directional driver–vehicle–bridge interaction model, and the vertical and torsional impact effects at the third span midpoint are greater than the lateral impact effect.

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16

Kanaan, Dina, Suzan Ayas, Birsen Donmez, Martina Risteska, and Joyita Chakraborty. "Using Naturalistic Vehicle-Based Data to Predict Distraction and Environmental Demand." International Journal of Mobile Human Computer Interaction 11, no. 3 (July 2019): 59–70. http://dx.doi.org/10.4018/ijmhci.2019070104.

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This research utilized vehicle-based measures from a naturalistic driving dataset to detect distraction as indicated by long off-path glances (≥ 2 s) and whether the driver was engaged in a secondary (non-driving) task or not, as well as to estimate motor control difficulty associated with the driving environment (i.e. curvature and poor surface conditions). Advanced driver assistance systems can exploit such driver behavior models to better support the driver and improve safety. Given the temporal nature of vehicle-based measures, Hidden Markov Models (HMMs) were utilized; GPS speed and steering wheel position were used to classify the existence of off-path glances (yes vs. no) and secondary task engagement (yes vs. no); lateral (x-axis) and longitudinal (y-axis) acceleration were used to classify motor control difficulty (lower vs. higher). Best classification accuracies were achieved for identifying cases of long off-path glances and secondary task engagement with both accuracies of 77%.

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Čulík, Kristián, Alica Kalašová, and Simona Kubíková. "Simulation as an Instrument for Research of Driver-vehicle Interaction." MATEC Web of Conferences 134 (2017): 00008. http://dx.doi.org/10.1051/matecconf/201713400008.

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Aldridge, L. C., and T. C. Lansdown. "Driver Preferences for Speech Based Interaction with in-Vehicle Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 43, no. 18 (September 1999): 977–81. http://dx.doi.org/10.1177/154193129904301807.

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Bavendiek, Jan, Emily Oliveira, and Lutz Eckstein. "Method for Development of Metaphor-based Driver-vehicle Interaction Concepts." ATZelectronics worldwide 14, no. 9 (September 2019): 52–55. http://dx.doi.org/10.1007/s38314-019-0087-4.

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Spelta, Cristiano, Vincenzo Manzoni, Andrea Corti, Andrea Goggi, and Sergio Matteo Savaresi. "Smartphone-Based Vehicle-to-Driver/Environment Interaction System for Motorcycles." IEEE Embedded Systems Letters 2, no. 2 (June 2010): 39–42. http://dx.doi.org/10.1109/les.2010.2052019.

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21

Horrey, William J., and John D. Lee. "Preface to the Special Issue on Human Factors and Advanced Vehicle Automation: Of Benefits, Barriers, and Bridges to Safe and Effective Implementation." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 2 (March 2020): 189–93. http://dx.doi.org/10.1177/0018720820901542.

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Objective The aim of this special issue is to bring together the latest research related to driver interaction with various types of vehicle automation. Background Vehicle technology has undergone significant progress over the past decade, bringing new support features that can assist the driver and take on more and more of the driving responsibilities. Method This issue is comprised of eight articles from international research teams, focusing on different types of automation and different user populations, including driver support features through to highly automated driving systems. Results The papers comprising this special issue are clustered into three categories: (a) experimental studies of driver interactions with advanced vehicle technologies; (b) analysis of existing data sources; and (c) emerging human factors issues. Studies of currently available and pending systems highlight some of the human factors challenges associated with the driver–system interaction that are likely to become more prominent in the near future. Moreover, studies of more nascent concepts (i.e., those that are still a long way from production vehicles) underscore many attitudes, perceptions, and concerns that will need to be considered as these technologies progress. Conclusions Collectively, the papers comprising this special issue help fill some gaps in our knowledge. More importantly, they continue to help us identify and articulate some of the important and potential human factors barriers, design considerations, and research needs as these technologies become more ubiquitous.

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Woo, Hanwool, Mizuki Sugimoto, Hirokazu Madokoro, Kazuhito Sato, Yusuke Tamura, Atsushi Yamashita, and Hajime Asama. "Goal Estimation of Mandatory Lane Changes Based on Interaction between Drivers." Applied Sciences 10, no. 9 (May 8, 2020): 3289. http://dx.doi.org/10.3390/app10093289.

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In this paper, we propose a novel method to estimate a goal of surround vehicles to perform a lane change at a merging section. Recently, autonomous driving and advance driver-assistance systems are attracting great attention as a solution to substitute human drivers and to decrease accident rates. For example, a warning system to alert a lane change performed by surrounding vehicles to the front space of the host vehicle can be considered. If it is possible to forecast the intention of the interrupting vehicle in advance, the host driver can easily respond to the lane change with sufficient reaction time. This paper assumes a mandatory situation where two lanes are merged. The proposed method assesses the interaction between the lane-changing vehicle and the host vehicle on the mainstream lane. Then, the lane-change goal is estimated based on the interaction under the assumption that the lane-changing driver decides to minimize the collision risk. The proposed method applies the dynamic potential field method, which changes the distribution according to the relative speed and distance between two subject vehicles, to assess the interaction. The performance of goal estimation is evaluated using real traffic data, and it is demonstrated that the estimation can be successfully performed by the proposed method.

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Podoprigora, N. V. "Structure and functioning of the DVRE system («Driver-Vehicle-Road-Environment»)." Вестник гражданских инженеров 19, no. 2 (2022): 154–59. http://dx.doi.org/10.23968/1999-5571-2022-19-2-154-159.

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The article presents the research results dealing with information aspects of the DVRE system («Driver-Vehicle-Road-Environment»). There has been worked out a unified information model of interaction of the driver (D) with other links of the «DVRE» system. The author analyzes the information signals coming to the driver from the controlled vehicle (V), the road (R), the environment (E), and other road traffic participants (RTP). There are constructed channels of information exchange between the links and individual subsystems. The classic DVRE system («Driver-Vehicle-Road-Environment»), taking into account the proposed changes, is regarded by the author as a unified holistic system RTP-V-R-E («Road Traffic Participants-Vehicle -Road-Environment»). Scientific novelty lies in the possibility of analyzing and modeling the information exchange between the driver (D) and other subsystems of the RTP-V-R-E system («Road Traffic Participants-Vehicle-Road-Environment») in order to forecast and prevent road traffic accidents.

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Kashevnik, Alexey, Andrew Ponomarev, Nikolay Shilov, and Andrey Chechulin. "Threats Detection during Human-Computer Interaction in Driver Monitoring Systems." Sensors 22, no. 6 (March 19, 2022): 2380. http://dx.doi.org/10.3390/s22062380.

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This paper presents an approach and a case study for threat detection during human–computer interaction, using the example of driver–vehicle interaction. We analyzed a driver monitoring system and identified two types of users: the driver and the operator. The proposed approach detects possible threats for the driver. We present a method for threat detection during human–system interactions that generalizes potential threats, as well as approaches for their detection. The originality of the method is that we frame the problem of threat detection in a holistic way: we build on the driver–ITS system analysis and generalize existing methods for driver state analysis into a threat detection method covering the identified threats. The developed reference model of the operator–computer interaction interface shows how the driver monitoring process is organized, and what information can be processed automatically, and what information related to the driver behavior has to be processed manually. In addition, the interface reference model includes mechanisms for operator behavior monitoring. We present experiments that included 14 drivers, as a case study. The experiments illustrated how the operator monitors and processes the information from the driver monitoring system. Based on the case study, we clarified that when the driver monitoring system detected the threats in the cabin and notified drivers about them, the number of threats was significantly decreased.

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Santolino, Miguel, Luis Céspedes, and Mercedes Ayuso. "The Impact of Aging Drivers and Vehicles on the Injury Severity of Crash Victims." International Journal of Environmental Research and Public Health 19, no. 24 (December 19, 2022): 17097. http://dx.doi.org/10.3390/ijerph192417097.

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Against a general trend of increasing driver longevity, the injuries suffered by vehicle occupants in Spanish road traffic crashes are analyzed by the level of severity of their bodily injuries (BI). Generalized linear mixed models are applied to model the proportion of non-serious, serious, and fatal victims. The dependence between vehicles involved in the same crash is captured by including random effects. The effect of driver age and vehicle age and their interaction on the proportion of injured victims is analyzed. We find a nonlinear relationship between driver age and BI severity, with young and older drivers constituting the riskiest groups. In contrast, the expected severity of the crash increases linearly up to a vehicle age of 18 and remains constant thereafter at the highest level of BI severity. No interaction between the two variables is found. These results are especially relevant for countries such as Spain with increasing driver longevity and an aging car fleet.

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Feierle, Alexander, Michael Rettenmaier, Florian Zeitlmeir, and Klaus Bengler. "Multi-Vehicle Simulation in Urban Automated Driving: Technical Implementation and Added Benefit." Information 11, no. 5 (May 19, 2020): 272. http://dx.doi.org/10.3390/info11050272.

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This article investigates the simultaneous interaction between an automated vehicle (AV) and its passenger, and between the same AV and a human driver of another vehicle. For this purpose, we have implemented a multi-vehicle simulation consisting of two driving simulators, one for the AV and one for the manual vehicle. The considered scenario is a road bottleneck with a double-parked vehicle either on one side of the road or on both sides of the road where an AV and a simultaneously oncoming human driver negotiate the right of way. The AV communicates to its passenger via the internal automation human–machine interface (HMI) and it concurrently displays the right of way to the human driver via an external HMI. In addition to the regular encounters, this paper analyzes the effect of an automation failure, where the AV first communicates to yield the right of way and then changes its strategy and passes through the bottleneck first despite oncoming traffic. The research questions the study aims to answer are what methods should be used for the implementation of multi-vehicle simulations with one AV, and if there is an added benefit of this multi-vehicle simulation compared to single-driver simulator studies. The results show an acceptable synchronicity for using traffic lights as basic synchronization and a distance control as the detail synchronization method. The participants had similar passing times in the multi-vehicle simulation compared to a previously conducted single-driver simulation. Moreover, there was a lower crash rate in the multi-vehicle simulation during the automation failure. Concluding the results, the proposed method seems to be an appropriate solution to implement multi-vehicle simulation with one AV. Additionally, multi-vehicle simulation offers a benefit if more than one human affects the interaction within a scenario.

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27

Burnette, Charles, and William Schaaf. "Issues in Using Jack Human Figure Modeling Software To Assess Human-Vehicle Interaction in a Driving Simulator." Transportation Research Record: Journal of the Transportation Research Board 1631, no. 1 (January 1998): 1–7. http://dx.doi.org/10.3141/1631-01.

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The strengths and weaknesses of a leading human figure modeling software when applied to real-time modeling and assessment of human-vehicle interaction in a driving simulator are described. The Jack human figure modeling software, developed at the Center for Human Modeling and Simulation at the University of Pennsylvania, is being applied by the Advanced Driver Interface Design/Assessment Project at the Graduate Program in Industrial Design, the University of the Arts, Philadelphia, to (1) model human-vehicle interactions in driving scenarios before implementing the scenarios in a driving simulator, (2) model the anthropometry of actual drivers in relationship to an accurate computer model of the vehicle interface, (3) capture real-time human-simulator interaction in a dynamic model of human movement and visual attention during a driving episode, (4) select and manipulate discrete objects and behaviors within the model, and (5) replay the model with anthropometric data representing a hypothetical driver population. The difficulties and benefits encountered when trying to use the software and the work under way to develop integrated procedures and tools to support dynamic modeling and analysis of human-vehicle interactions are outlined as an introduction for researchers considering the use of interactive human modeling and simulation software in the assessment of human factors in complex operating environments.

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28

Mozaffari, Hamed, and Ali Nahvi. "A motivational driver model for the design of a rear-end crash avoidance system." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 1 (May 23, 2019): 10–26. http://dx.doi.org/10.1177/0959651819847380.

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A motivational driver model is developed to design a rear-end crash avoidance system. Current driver assistance systems use engineering methods without considering psychological human aspects, which leads to false activation of assistance systems and complicated control algorithms. The presented driver model estimates driver’s psychological motivations using the combined longitudinal and lateral time to collision, the vehicle kinematics, and the vehicle dynamics. These motivations simplify both autonomous driving algorithms and human-machine interactions. The optimal point of a motivational multi-objective cost function defines the decision for the autonomous driving. Moreover, the motivations are used as risk assessment factors for driver–machine interaction in dangerous situations. The system is evaluated on 10 human subjects in a driving simulator. The assistance system had no false activation during the tests. It avoided collisions in all the rear-end crash avoidance scenarios, while 90% of human subjects did not.

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González-Saavedra, Juan Felipe, Miguel Figueroa, Sandra Céspedes, and Samuel Montejo-Sánchez. "Survey of Cooperative Advanced Driver Assistance Systems: From a Holistic and Systemic Vision." Sensors 22, no. 8 (April 15, 2022): 3040. http://dx.doi.org/10.3390/s22083040.

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The design of cooperative advanced driver assistance systems (C-ADAS) involves a holistic and systemic vision that considers the bidirectional interaction among three main elements: the driver, the vehicle, and the surrounding environment. The evolution of these systems reflects this need. In this work, we present a survey of C-ADAS and describe a conceptual architecture that includes the driver, vehicle, and environment and their bidirectional interactions. We address the remote operation of this C-ADAS based on the Internet of vehicles (IoV) paradigm, as well as the involved enabling technologies. We describe the state of the art and the research challenges present in the development of C-ADAS. Finally, to quantify the performance of C-ADAS, we describe the principal evaluation mechanisms and performance metrics employed in these systems.

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30

AKINYEDE, Raphael. "Modeling of Situation Response Time in Vehicular Ad-Hoc Network." MATICS 10, no. 1 (September 25, 2018): 1. http://dx.doi.org/10.18860/mat.v10i1.4785.

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— In Vehicular Ad-Hoc Networks (VANETs), wireless-equipped vehicles form a network spontaneously while traveling along the road. The direct wireless transmission from vehicle to vehicle makes it possible for them to communicate even where there is no telecommunication infrastructure; this emerging new technology provide ubiquitous connectivity to vehicular nodes while on the move, The main idea is to provide ubiquitous connectivity to vehicular nodes while on the move, and to create efficient vehicle-to-vehicle communications that enable the Intelligent Transportation Systems (ITS). This is achieved by allowing nodes within certain ranges to connect with each other in order to exchange information. Since accident happens in split seconds, to avoid communication inefficiency, there is need for this information to get to the intended vehicle on time. To solve this problem, this work models each vehicle in a chain of others and how it responds to the traffic around it using Microscopic (also known as car-following) method for modeling traffic flow; driver- to-driver and driver-to-road interactions within a traffic stream and the interaction between a driver and another driver on road were considered. The essence of this modeling is to determine the minimum response time required for a vehicle in VANET to respond and communicate situations on the road. A simulated scenario was carried out for two vehicles, a leading vehicle and following vehicle. The result shows that with an average of 32 meters apart with average difference in velocity of 1.23m/s, a minimum of 0.9secs is required for efficient situation response communication to ensue between them.

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Wang, Tao, Yuzhi Chen, Xingchen Yan, Jun Chen, and Wenyong Li. "The Relationship between Bus Drivers’ Improper Driving Behaviors and Abnormal Vehicle States Based on Advanced Driver Assistance Systems in Naturalistic Driving." Mathematical Problems in Engineering 2020 (August 20, 2020): 1–12. http://dx.doi.org/10.1155/2020/9743504.

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In order to improve the adaptation of driver to the advanced driver assistance system (ADAS) and optimize the active safety control technology of vehicle under man-computer cooperative driving, this paper investigated the correlation between driver’s improper driving behaviors and abnormal vehicle states under the ADAS. Based on the warning data collected from the driver’s assistance warning system equipped on buses, the interaction between improper behaviors, between abnormal vehicle states, and between improper behaviors and abnormal vehicle states were quantitatively analyzed through the hierarchical clustering method and improved Apriori algorithm. The results showed that eye closure and yawn were high in concurrency (probability: 0.888) and interaction (average probability: 0.946); the interaction among lane departure, rapid acceleration, and rapid deceleration are frequent (average probability: 0.7224); eye closure (average probability: 0.452) and yawn (average probability: 0.444) are likely to induce abnormal vehicle states such as rapid acceleration and rapid deceleration. Some suggestions proposed based on the results are as follows. First, it is suggested that the ADAS should combine the warning modes of eye closure and yawn; second, when the driver closes eyes or yawns, the control of the ADAS over the lateral and longitudinal performance of vehicle should be enhanced; third, the extent of control by the ADAS should be determined according to the relationship probability; finally, the lateral control over the vehicle by the ADAS should be strengthened when there is a forward collision warning.

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Chen, Tao, and Lang Wei. "Development of a Virtual Proving Ground for Commercial Vehicle." Advanced Materials Research 457-458 (January 2012): 1529–35. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.1529.

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Virtual proving ground (VPG) are used effectively for commercial vehicle system development, human factor study, and other purposes by enabling to reproduce actual driving conditions in a safe and tightly controlled environment. This paper describes a virtual proving ground developed for design and evaluation of commercial vehicle and for driver-vehicle interaction study. VPG consists of a real-time vehicle simulation system, a visual and audio system, a driver handling signals acquisition system providing a realistic interface between the operator and the simulated environment, and 3D proving ground databases with areas suitable for various types of vehicle test tasks. The real-time vehicle simulation system simulates dynamic motion of realistic vehicle models in real-time. The visual system generates high fidelity driving scenes. The handling signals collection system acquires the steering, braking, accelerating, and shifting operation of driver. The pilot experiments carried out in the areas of vehicle handling and stability study are also presented to show the effectiveness of the developed VPG.

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Tucă, Alexandra, Valerian Croitorescu, Mircea Oprean, and Thomas Brandemeir. "Driving Simulators for Human Vehicle Interaction Design." Balkan Region Conference on Engineering and Business Education 1, no. 1 (November 1, 2015): 297–306. http://dx.doi.org/10.1515/cplbu-2015-0034.

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AbstractThe interaction human-vehicle, as well as driver’s behavior are subject long debated in the automotive engineering domain. Driving simulators have an extraordinary important role allowing research that would not be possible to study in real world scenarios.A driver uses his sensory inputs to obtain the required input to base his decision on. The bandwidth of the required input signal should be in accordance to the driver’s task. For simple tasks, like turning on the screen wipers or direction indicator, low frequency information is sufficient. High frequency information is required when cornering on a busy road or when driving in relatively limit situations.The optimal configuration of each sub-system remains a significant cause for debate and still poses a major challenge when considering the ability of simulators to extract realistic driver behavior. If a difference is observed between real and virtual conditions, the factors specifically cause these differences are very difficult to be explained.

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Nguyen, Anh-Tu, Jagat Jyoti Rath, Chen Lv, Thierry-Marie Guerra, and Jimmy Lauber. "Human-Machine Shared Driving Control for Semi-Autonomous Vehicles Using Level of Cooperativeness." Sensors 21, no. 14 (July 7, 2021): 4647. http://dx.doi.org/10.3390/s21144647.

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This paper proposes a new haptic shared control concept between the human driver and the automation for lane keeping in semi-autonomous vehicles. Based on the principle of human-machine interaction during lane keeping, the level of cooperativeness for completion of driving task is introduced. Using the proposed human-machine cooperative status along with the driver workload, the required level of haptic authority is determined according to the driver’s performance characteristics. Then, a time-varying assistance factor is developed to modulate the assistance torque, which is designed from an integrated driver-in-the-loop vehicle model taking into account the yaw-slip dynamics, the steering dynamics, and the human driver dynamics. To deal with the time-varying nature of both the assistance factor and the vehicle speed involved in the driver-in-the-loop vehicle model, a new ℓ∞ linear parameter varying control technique is proposed. The predefined specifications of the driver-vehicle system are guaranteed using Lyapunov stability theory. The proposed haptic shared control method is validated under various driving tests conducted with high-fidelity simulations. Extensive performance evaluations are performed to highlight the effectiveness of the new method in terms of driver-automation conflict management.

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Islinger, Tobias, Thorsten Köhler, and Christian Wolff. "A Functional Driver Analyzing Concept." Advances in Human-Computer Interaction 2011 (2011): 1–4. http://dx.doi.org/10.1155/2011/413964.

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It is evident that a lot of accidents occur because of drowsiness or inattentiveness of the driver. The logical consequence is that we have to find methods to better analyze the driver. A lot of research has been spent on camera-based systems which focus on the driver's eye gaze or his head movement. But there are few systems that provide camera-free driver analyzing. This is the main goal of the work presented here which is structured in three phases, with the operational goal of having a working driver analyzer implemented in a car. The main question is: is it possible to make statements concerning the driver and his state by using vehicle data from the CAN Bus only? This paper describes the current state of driver analyzing, our overall system architecture, as well as future work. At the moment, we focus on detecting the driving style of a person.

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Han, Junyan, Xiaoyuan Wang, and Gang Wang. "Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review." Sustainability 14, no. 13 (July 5, 2022): 8179. http://dx.doi.org/10.3390/su14138179.

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Car-following behavior is the result of the interaction of various elements in the specific driver-vehicle-environment aggregation. Under the intelligent and connected condition, the information perception ability of vehicles has been significantly enhanced, and abundant information about the driver-vehicle-environment factors can be obtained and utilized to study car-following behavior. Therefore, it is necessary to comprehensively take into account the driver-vehicle-environment factors when modeling car-following behavior under intelligent and connected conditions. While there are a considerable number of achievements in research on car-following behavior, a car-following model with comprehensive consideration of driver-vehicle-environment factors is still absent. To address this gap, the literature with a focus on car-following behavior research with consideration of the driver, vehicle, or environment were reviewed, the contributions and limitations of the previous studies were analyzed, and the future exploration needs and prospects were discussed in this paper. The results can help understand car-following behavior and the traffic flow characteristics affected by various factors and provide a reference for the development of traffic flow theory towards smart transportation systems and intelligent and connected driving.

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Gholami, Amir, and Majid Majidi. "Development of a neuromuscular driver model with an estimation of steering torque feedback in vehicle steer-by-wire systems." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 233, no. 3 (March 12, 2019): 657–67. http://dx.doi.org/10.1177/1464419319829980.

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In this paper, a neuromuscular driver model for sensing torque feedback or haptic interaction between the vehicle equipped with steer-by-wire (SBW) system and the driver has been developed. The proposed driver model consists of a preview model and a neuromuscular model. The preview driver model calculates the desired angle of the steering-wheel to follow the path, and the neuromuscular driver model, with the ability of perceiving real-time torque feedback, determines the real angle of the steering-wheel angle according to muscular system transfer functions to follow the desired steering-wheel angle. In order to calculate torques on the steering-wheel, the lateral tyre-road forces are estimated by Kalman filter designed using a linear 2-DOF vehicle model. So, the design of the neuromuscular driver model combined with torque feedback estimation is the main contribution of this paper. The simulation results from TruckSim and Simulink software indicate that the novel designed driver model with torque feedback estimation has an important role in the controlling and steering vehicle to follow the desired paths.

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Khudiakov, Igor, Igor Gritsuk, Valentina Chernenko, Yriy Gritsuk, Dmytro Pohorletskyi, Tamara Makarova, and Viktor Manzhelei. "Features of modeling and construction of the information system of remote monitoring of the technical condition of vehicles." Journal of Mechanical Engineering and Transport 14, no. 2 (January 2022): 140–48. http://dx.doi.org/10.31649/2413-4503-2021-14-2-140-148.

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The article presents the features of construction and modeling of the system of operational remote monitoring of the technical condition of the truck in operation, and ensuring the interaction of monitoring data of the technical condition of the vehicle, trailer, mode of operation and rest of the driver and physical condition of the driver. The peculiarity of the vehicle monitoring information system equipped with the means of registration, mode of work and rest of the driver and physical condition of the driver is that it considers the features of remote inspection of the mode of work and rest of the driver and physical condition of the driver in the modern information and communication complex. means. The on-board intelligent diagnostic complex allows to measure in the conditions of operation a large number of parameters of the vehicle with the internal combustion engine and to carry out their registration on the remote computer with use of possibilities of the claimed method. As a result of forming a model of information system for monitoring the technical condition of the vehicle, it is possible to simultaneously monitor the parameters of the vehicle, provide remote inspection of driver's work and rest, physical condition of the driver, environmental performance of the vehicle, speeding. The process of formation and analysis of information structures of information-analytical system of operative control of technical condition of vehicle in operating conditions (Systems of Operative Control of a Technical Condition of the Vehicle in Operating Conditions) is considered. The effectiveness of the remote monitoring information system is enhanced by the possibility of prompt adjustment of the level of negative impact of motor vehicles on the environment and road infrastructure.

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Yan, Xuedong, Essam Radwan, and Elizabeth Birriel. "Analysis of Red Light Running Crashes Based on Quasi-Induced Exposure and Multiple Logistic Regression Method." Transportation Research Record: Journal of the Transportation Research Board 1908, no. 1 (January 2005): 70–79. http://dx.doi.org/10.1177/0361198105190800109.

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According to recent national statistics, red light running crashes represent a significant safety problem at signalized intersections. To examine the overall characteristics of red light running crashes, this study used the 1999 to 2001 Florida crash database to investigate the crash propensity related to traffic environments, driver characteristics, and vehicle types. The quasi-induced exposure concept and multiple logistic regression technique were used to perform this analysis. The results showed that traffic factors including number of lanes, crash time, weather, highway character, day of week, urban or rural location, speed limit, driver age, alcohol or drug use, physical defect, driver residence, and vehicle type were significantly associated with the risk of red light running crashes. Furthermore, it confirmed that there were significant interaction effects between the risk factors, including crash time and highway character, number of lanes and urban or rural location, weather condition and driver age, driver age and gender, alcohol or drug use and gender, and type of vehicle and gender.

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40

Hassan, Rayya A., and Kerry McManus. "Assessment of Interaction Between Road Roughness and Heavy Vehicles." Transportation Research Record: Journal of the Transportation Research Board 1819, no. 1 (January 2003): 236–43. http://dx.doi.org/10.3141/1819b-30.

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Road surface roughness excites low- and high-frequency vibration modes of a heavy articulated vehicle body. These vibrations result in motions in all directions that detract from the driver’s perceived ride and comfort and increase pavement damage due to dynamic wheel loads (DWLs). A subjective assessment survey was conducted to identify surface roughness characteristics that mainly influence the perceptions of heavy-vehicle drivers of pavement rideability and their comfort. The latter was achieved by correlating drivers’ ratings to roughness contents in different roughness wavebands. The results indicated that the drivers mainly object to low-frequency body vibrations excited by roughness wavelengths in the range of 4.88 to 19.5 m. Roughness content in this band was used to establish a new profile-based index called the profile index for truck ( PIt). Drivers consider pavement rideability to be poor when PIt exceeds 2.75 m/km. PIt provides better predictions of heavy vehicle ride than the international roughness index (IRI). The methodology for developing the PIt and assessment of its reliability as a measure of heavy vehicle ride are described. The latter was achieved by testing the statistical significance of the effects of factors other than road roughness that influence the perceived ride of truck drivers. They include factors related to the vehicle, the road, and the driver as well as situational factors. In addition, PIt was found to be a better indicator than IRI of the levels of whole body vibrations transmitted to the driver through the seat and a better predictor of the magnitude of DWL to which the test pavements are subject.

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Chen, Wan-Hui, Chih-Yung Lin, and Ji-Liang Doong. "Effects of Interface Workload of In-Vehicle Information Systems on Driving Safety." Transportation Research Record: Journal of the Transportation Research Board 1937, no. 1 (January 2005): 73–78. http://dx.doi.org/10.1177/0361198105193700111.

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Driver distraction and lack of awareness of the driving situation are major causes of accidents in the urban areas in Taiwan; failing to obey traffic signals is the third leading accident cause. Numerous innovative in-vehicle information systems (IVIS) could be used collectively to provide drivers with a variety of information, such as messages from intersection collision warning systems (ICWS) by way of different in-vehicle interfaces. How the different IVIS interfaces influence driver workload and safety is always an important issue. This study investigates the effects of auditory ICWS messages on driver performance while the driver's visual, hearing, or mental processing attention resources (or all three) are engaged by secondary tasks. This type of engagement or distraction commonly occurs when a driver uses IVIS. The secondary tasks used to distract drivers were created by different types of mathematical questions presented with different types of display devices (e.g., voice from a speaker or numbers shown on a liquid crystal display screen or head-up display). Mixed linear models were employed to examine the factors influencing driver perception–reaction time with the consideration of repeated measures. Several factors, including several main factors and an interaction, were found to be significant. The most important finding was that the interaction between provision of ICWS information and the display format indicated that an auditory warning message could increase driver perception–reaction time while a driver was distracted by an auditory task. In addition, it was found that driver distraction due to different mental processing tasks had a significant impact on driver perception–reaction time.

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42

Hagiya, Toshiyuki, and Kazunari Nawa. "Acceptability Evaluation of Inter-driver Interaction via a Vehicle Agent Using Vehicle-to-Vehicle Communication on a Driving Simulator." Journal of Information Processing 29 (2021): 667–75. http://dx.doi.org/10.2197/ipsjjip.29.667.

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43

Hagiya, Toshiyuki, and Kazunari Nawa. "Acceptability Evaluation of Inter-driver Interaction via a Vehicle Agent Using Vehicle-to-Vehicle Communication on a Driving Simulator." Journal of Information Processing 29 (2021): 667–75. http://dx.doi.org/10.2197/ipsjjip.29.667.

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44

Yang, Junru, Duanfeng Chu, Rukang Wang, Meng Gao, and Chaozhong Wu. "Coupling effect modeling of driver vehicle environment factors influencing speed selections in curves." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 7 (August 27, 2019): 2066–78. http://dx.doi.org/10.1177/0954407019870349.

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It is of significant importance to select an appropriate speed for a vehicle to drive through an upcoming curve. Previous studies have mainly taken into account the vehicle–road interaction, which lacks quantitative analysis of drivers’ driving behavior related to curve speed selections. In this study, a curve speed model derived from the vehicle–road coupling effect analysis is combined with drivers’ driving styles which are classified into aggressive and moderate styles. Moreover, a driver behavior questionnaire based analysis is carried out for quantitative identification of the above two groups of drivers, compared with the traditional vehicle-motion-indexed classification of driving styles. Unlike previous curve speed models, the proposed model not only takes the vehicle–road coupling effect into consideration, but also introduces a driving style factor which is quantified with both driver behavior questionnaire analysis and vehicle-motion-indexed classification. The proposed curve speed model was validated with the road test data. It is found that the proposed curve speed model considering both the vehicle–road interaction and drivers’ driving styles could effectively guarantee traffic safety and riding comfort in sharp curves.

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45

Vasconez, Juan Pablo, Diego Carvajal, and Fernando Auat Cheein. "On the design of a human–robot interaction strategy for commercial vehicle driving based on human cognitive parameters." Advances in Mechanical Engineering 11, no. 7 (July 2019): 168781401986271. http://dx.doi.org/10.1177/1687814019862715.

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A proper design of human–robot interaction strategies based on human cognitive factors can help to compensate human limitations for safety purposes. This work is focused on the development of a human–robot interaction system for commercial vehicle (Renault Twizy) driving, that uses driver cognitive parameters to improve driver’s safety during day and night tasks. To achieve this, eye blink behavior measurements are detected using a convolutional neural network, which is capable of operating under variable illumination conditions using an infrared camera. Percentage of eye closure measure values along with blink frequency are used to infer diver’s sleepiness level. The use of such algorithm is validated with experimental tests for subjects under different sleep-quality conditions. Additional cognitive parameters are also analyzed for the human–robot interaction system such as driver sleep quality, distraction level, stress level, and the effects related to not wearing glasses. Based on such driver cognitive state parameters, a human–robot interaction strategy is proposed to limit the speed of a Renault Twizy vehicle by intervening its acceleration and braking system. The proposed human–robot interaction strategy can increase safety during driving tasks for both users and pedestrians.

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46

Kaligin, Nikolay N., Saygid U. Uvaysov, Aida S. Uvaysova, and Svetlana S. Uvaysova. "Infrastructural review of the distributed telecommunication system of road traffic and its protocols." Russian Technological Journal 7, no. 6 (January 10, 2020): 87–95. http://dx.doi.org/10.32362/2500-316x-2019-7-6-87-95.

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To organize an efficient transport structure, modern road telecommunication systems provide information collection about the vehicle connected to the system and analyze it. The modern car in such a system is considered to be connected. Such information systems can collect information about the vehicle. This information includes its driving parameters, location, and the parameters of the vehicle systems state. After processing and analyzing this information, it is possible to form recommendations and control actions. These recommendations are used by the driver or an automated vehicle control system. This article describes the general principle of the operation of modern transport telecommunication systems. The car-to-car type of interaction protocols are highlighted in this system. Wireless communication technologies that allow this interaction to be implemented are described. One of the principles was developed, according to which the system can determine the optimal use of the vehicle resource and the aggressiveness of the driving style of a freight vehicle on the basis of an automated algorithm for issuing recommendations for driver actions. This principle is considered as exemplified by a series of load characteristics of a diesel engine. The principle of choosing the optimal series of recommendations to a group of drivers to optimize the movement of traffic through the car-tocar interaction has been formulated.

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47

Ponnan, Suresh, J. Robert Theivadas, HemaKumar VS, and Daniel Einarson. "Driver monitoring and passenger interaction system using wearable device in intelligent vehicle." Computers and Electrical Engineering 103 (October 2022): 108323. http://dx.doi.org/10.1016/j.compeleceng.2022.108323.

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48

Hollnagel, Erik. "A function-centred approach to joint driver-vehicle system design." Cognition, Technology & Work 8, no. 3 (April 7, 2006): 169–73. http://dx.doi.org/10.1007/s10111-006-0032-1.

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49

Yang, Shiyan, Michael G. Lenné, Bryan Reimer, and Pnina Gershon. "Modeling Driver-Automation Interaction using A Naturalistic Multimodal Driving Dataset." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (September 2022): 1462–66. http://dx.doi.org/10.1177/1071181322661084.

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This paper explores a holistic approach to understanding the impacts of on-road factors on continuous driver-automation interaction in naturalistic environments. We processed and synchronized CAN Bus signals, GPS coordinates, and high-definition videos collected from two drivers in 98 trips (~97 hours) involving Tesla Autopilot engagement collected over three years as part of the MIT Advanced Vehicle Technology naturalistic driving study. Bayesian generalized linear models were trained with the synchronized data and revealed that steering wheel rotations and speed changes, even to a small extent, cause Autopilot disengagement. Road types and experience were also associated with drivers’ probability of using driver assistance, while speed and surrounding vehicles had little impact (when traveling in stable states). This data-driven approach enables a more comprehensive understanding of driver-automation interaction to enhance safety.

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50

Kamezaki, Mitsuhiro, Hiroaki Hayashi, Udara E. Manawadu, and Shigeki Sugano. "Human-Centered Intervention Based on Tactical-Level Input in Unscheduled Takeover Scenarios for Highly-Automated Vehicles." International Journal of Intelligent Transportation Systems Research 18, no. 3 (December 16, 2019): 451–60. http://dx.doi.org/10.1007/s13177-019-00217-x.

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AbstractDue to functional limitations in certain situations, the driver receives a request to intervene from automated vehicles operating level 3. Unscheduled intervention of control authority would lead to insufficient situational awareness, then this will make dangerous situations. The purpose of this study is thus to propose tactical-level input (TLI) method with a multimodal driver-vehicle interface (DVI) for the human-centered intervention. The proposed DVI system includes touchscreen, hand-gesture, and haptic interfaces that enable interaction between driver and vehicle, and TLI along with such DVI system can enhance situational awareness. We performed unscheduled takeover experiments using a driving simulator to evaluate the proposed intervention system. The experimental results indicate that TLI can reduce reaction time and driver workload, and moreover, most drivers preferred the use of TLI than manual takeover.

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