Driving Simulator System to Evaluate Driver’s Workload Using ADAS in Different Driving Contexts

2017 ◽  
Author(s):  
Giandomenico Caruso ◽  
Daniele Ruscio ◽  
Dedy Ariansyah ◽  
Monica Bordegoni
Keyword(s):  
Driving Simulator ◽  
Driving Simulation ◽  
Human Cognition ◽  
Driving Safety ◽  
Physiological Data ◽  
Driver Assistance Systems ◽  
Safe Driving ◽  
Dangerous Driving ◽  
Vehicle Technology ◽  
Physiological Sensors

The advancement of in-vehicle technology for driving safety has considerably improved. Current Advanced Driver-Assistance Systems (ADAS) make road safer by alerting the driver, through visual, auditory, and haptic signals about dangerous driving situations, and consequently, preventing possible collisions. However, in some circumstances the driver can fail to properly respond to the alert since human cognition systems can be influenced by the driving context. Driving simulation can help evaluating this aspect since it is possible to reproduce different ADAS in safe driving conditions. However, driving simulation alone does not provide information about how the change in driver’s workload affects the interaction of the driver with ADAS. This paper presents a driving simulator system integrating physiological sensors that acquire heart’s activity, blood volume pulse, respiration rate, and skin conductance parameters. Through a specific processing of these measurements, it is possible to measure different cognitive processes that contribute to the change of driver’s workload while using ADAS, in different driving contexts. The preliminary studies conducted in this research show the effectiveness of this system and provide guidelines for the future acquisition and the treatment of the physiological data to assess ADAS workload.

scholarly journals A Driving Simulation to Analysis and Quantitative Comparison of Driving Behavior of Guide Signs at Intersections

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tianzheng Wei ◽  
Tong Zhu ◽  
Chenxin Li ◽  
Haoxue Liu
Keyword(s):  
Quantitative Evaluation ◽  
Evaluation Method ◽  
Driving Simulator ◽  
Current System ◽  
Driving Behavior ◽  
Driving Simulation ◽  
Analysis Method ◽  
Safe Driving ◽  
Amount Of Information ◽  
Significant Difference

Guide signs are an important source for drivers to obtain road information. However, the evaluation methods for the effectiveness of guide signs are not unified. The quantitative model for evaluating guide signs needs to be constructed to unify the current system of guide signs. This study aims to take the commonly used guide signs in China as the research object to explore the evaluation method of guide signs at intersections. Eight kinds of guide signs were designed and made based on the common layout (layout 1 and layout 2) and the amount of information on signs (3–6). Thirty-four drivers were recruited to organize a driving simulation based on the visual cognitive tasks. Drivers’ legibility time and driver behavior were obtained by using the driving simulator and E-Prime program. A comprehensive quantitative evaluation model of guide signs was established based on the factor analysis method and grey correlation analysis method from the perspective of safe driving. The results show that there is no significant difference in the SD of speed and the SD of acceleration under the influence of various guide signs. The average vehicle speed and acceleration decrease, and the lateral offset distance of the vehicle increases with the amount of information on guide signs increasing. The quantitative evaluation results of guide signs show that the visual security decreases with the increase of the amount of information on guide signs. And layout 2 has better performance than layout 1 when the amount of information on guide signs is the same. This study not only explores the change rule of driving behavior under the influence of guide signs, but also provides a reference for the selection of guide signs.

scholarly journals Neural correlates of safe driving performance: An fMRI study featuring a pedestrian-rich simulator environment

2021 ◽  
Author(s):  
Kentaro Oba ◽  
Koji Hamada ◽  
Azumi Tanabe-Ishibashi ◽  
Fumihiko Murase ◽  
Masaaki Hirose ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Social Cognitive ◽  
Driving Simulator ◽  
Neural Correlates ◽  
Driving Performance ◽  
Driving Safety ◽  
Facilitatory Effect ◽  
Safe Driving ◽  
Car Accidents ◽  
Fmri Study

Distracted attention is considered responsible for most car accidents, and many functional magnetic resonance imaging (fMRI) researchers have addressed its neural correlates using a car-driving simulator. Previous studies, however, have not directly addressed safe driving performance and did not place pedestrians in the simulator environment. In this fMRI study, we simulated a pedestrian-rich environment to explore the neural correlates of three types of safe driving performance: driving accuracy, the braking response to a preceding car, and the braking response to a crossing pedestrian. Activation of the bilateral frontoparietal control network predicted high driving accuracy. On the other hand, activation of the left posterior and right anterior superior temporal sulci preceding a sudden pedestrian crossing predicted a slow braking response. The results suggest the involvement of different cognitive processes in different components of driving safety: the facilitatory effect of maintained attention on driving accuracy and the distracting effect of social–cognitive processes on the braking response to pedestrians.

scholarly journals A Recognition Model of Driving Risk Based on Belief Rule-Base Methodology

2018 ◽  
Vol 32 (11) ◽  
pp. 1850037 ◽  
Author(s):  
Chuan Sun ◽  
Chaozhong Wu ◽  
Duanfeng Chu ◽  
Zhenji Lu ◽  
Jian Tan ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Data Driven ◽  
Driving Safety ◽  
Rule Base ◽  
Driver Assistance Systems ◽  
Recognition Model ◽  
Time Operation ◽  
Belief Rule Base ◽  
Driving Risk ◽  
Data Driven Modeling

This paper aims to recognize driving risks in individual vehicles online based on a data-driven methodology. Existing advanced driver assistance systems (ADAS) have difficulties in effectively processing multi-source heterogeneous driving data. Furthermore, parameters adopted for evaluating the driving risk are limited in these systems. The approach of data-driven modeling is investigated in this study for utilizing the accumulation of on-road driving data. A recognition model of driving risk based on belief rule-base (BRB) methodology is built, predicting driving safety as a function of driver characteristics, vehicle state and road environment conditions. The BRB model was calibrated and validated using on-road data from 30 drivers. The test results show that the recognition accuracy of our proposed model can reach about 90% in all situations with three levels (none, medium, large) of driving risks. Furthermore, the proposed simplified model, which provides real-time operation, is implemented in a vehicle driving simulator as a reference for future ADAS and belongs to research on artificial intelligence (AI) in the automotive field.

Improving Driving Safety by Detecting Negative Emotions with Biological Signals: Which Is the Best?

2021 ◽  
pp. 036119812110415
Author(s):  
Naser Habibifar ◽  
Hamed Salmanzadeh
Keyword(s):  
Driving Simulator ◽  
Electrodermal Activity ◽  
Negative Emotions ◽  
Evaluation Criteria ◽  
Driving Safety ◽  
Driver Assistance Systems ◽  
Ample Evidence ◽  
Biological Signals ◽  
Fear And Anxiety ◽  
Sensitivity Specificity

There is ample evidence confirming the adverse effects of negative emotions such as anger, fear, and anxiety on drivers’ performance. Also, effectiveness of biological signals in emotion recognition has been confirmed. Therefore, developing advanced driver-assistance systems based on biological signals to detect negative emotions can play a major role in improving driving safety. However, since recording signals, data analysis, as well as design and implementation of a system based on one or more biological signals take time and are costly, it is necessary to conduct appropriate preliminary studies on the efficiency of these signals in identifying negative emotions. The purpose of this study was to explore the efficiency of four biological signals including electrocardiogram (ECG), electromyogram (EMG), electrodermal activity (EDA), and electroencephalogram (EEG) in detecting negative emotions while driving. To this end, a series of scenarios were designed to arouse negative emotions in the driving simulator environment. A total of 43 individuals participated in the experiments, during which the four signals were recorded. Next, we extracted 58 features from the collected data for analysis. Then, multi-layer perceptron and radial basis function neural networks were implemented using the features of each of these signals separately. Afterward, the four evaluation criteria of accuracy, sensitivity, specificity, and precision were calculated for the signals. Finally, TOPSIS was used to rank the signals. ECG and EDA signals, with 88% and 90% accuracy, respectively, were found to be the best signals in detecting negative emotions during driving.

The Validity of Three New Driving Simulator Scenarios: Detecting Differences in Driving Performance by Difficulty and Driver Gender and Age

2020 ◽  
pp. 001872082093752
Author(s):  
Hillary Maxwell ◽  
Bruce Weaver ◽  
Sylvain Gagnon ◽  
Shawn Marshall ◽  
Michel Bédard
Keyword(s):  
Discriminant Validity ◽  
Driving Simulator ◽  
Age Groups ◽  
Driving Behavior ◽  
Driving Simulation ◽  
Driving Simulators ◽  
Safe Driving ◽  
Convergent And Discriminant Validity ◽  
Gender And Age ◽  
Driver Assessment

Objective We explored the convergent and discriminant validity of three driving simulation scenarios by comparing behaviors across gender and age groups, considering what we know about on-road driving. Background Driving simulators offer a number of benefits, yet their use in real-world driver assessment is rare. More evidence is needed to support their use. Method A total of 104 participants completed a series of increasingly difficult driving simulation scenarios. Linear mixed models were estimated to determine if behaviors changed with increasing difficulty and whether outcomes varied by age and gender, thereby demonstrating convergent and discriminant validity, respectively. Results Drivers adapted velocity, steering, acceleration, and gap acceptance according to difficulty, and the degree of adaptation differed by gender and age for some outcomes. For example, in a construction zone scenario, drivers reduced their mean velocities as congestion increased; males drove an average of 2.30 km/hr faster than females, and older participants drove more slowly than young (5.26 km/hr) and middle-aged drivers (6.59 km/hr). There was also an interaction between age and difficulty; older drivers did not reduce their velocities with increased difficulty. Conclusion This study provides further support for the ability of driving simulators to elicit behaviors similar to those seen in on-road driving and to differentiate between groups, suggesting that simulators could serve a supportive role in fitness-to-drive evaluations. Application Simulators have the potential to support driver assessment. However, this depends on the development of valid scenarios to benchmark safe driving behavior, and thereby identify deviations from safe driving behavior. The information gained through simulation may supplement other forms of assessment and possibly eliminate the need for on-road testing in some situations.

Advanced vehicle technology: Mapping mental model accuracy and system exposure to driver behavior

2021 ◽  
Vol 65 (1) ◽  
pp. 1072-1076
Author(s):  
Aaron Benson ◽  
Joanne But ◽  
John Gaspar ◽  
Cher Carney ◽  
William J. Horrey
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
Driving Simulator ◽  
Cruise Control ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Driver Education ◽  
The Road ◽  
Vehicle Technology ◽  
Assistance Systems

Advanced driver assistance systems have potential to increase safety and comfort for drivers; however, drivers need to understand the capabilities and limitations of these systems to use them appropriately. This study sought to explore how the quality (accuracy) of drivers’ mental models of adaptive cruise control (ACC) impacted their behavior and interactions while using the system. Seventy-eight participants drove in a high-fidelity driving simulator while operating an ACC system, in normal conditions and while interacting with the system interface. Participants with stronger (more accurate) mental models glanced to the road ahead more often during normal conditions early on compared to drivers were less accurate mental models; however, these differences diminished with increased system exposure. Glance behavior while interacting with the system and time to complete the interactions were less effected by the strength of the participant’s mental model. Results are discussed in the context of driver education and training.

scholarly journals A Novel Maneuver-Based Driving Envelope Generation Approach for Driving Safety Assessment

2021 ◽  
Vol 11 (10) ◽  
pp. 4702
Author(s):  
Bohan Jiang ◽  
Xiaohui Li ◽  
Yujun Zeng ◽  
Daxue Liu
Keyword(s):  
Driving Safety ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Simulation Experiments ◽  
Safe Driving ◽  
Human In The Loop ◽  
Envelope Method ◽  
Vehicle Motion ◽  
Assistance Systems ◽  
Real Vehicle

It is of utmost importance for advanced driver assistance systems to evaluate the risk of the current situation and make continuous decisions about what kind of evasive maneuver can be initiated. The purpose of this paper is to establish efficient indicators to evaluate the risk of candidate driving maneuvers for a human-in-the-loop vehicle. A novel safe driving envelope generation method is proposed, which takes various constraints into consideration, including the human operation, vehicle motion limits, and collision avoidance with road boundary and obstacles. The efficiency of the proposed method is validated by simulation experiments and real vehicle tests. The results show that the feasibility of candidate driving maneuvers can be efficiently determined by computing the driving envelope, and the proposed driving envelope method can be easily implemented for real-time applications.

Get Ready for Take-Overs: Using Head-Up Display for Drivers to Engage in Non–Driving-Related Tasks in Automated Vehicles

2021 ◽  
pp. 001872082110562
Author(s):  
Xiaomeng Li ◽  
Ronald Schroeter ◽  
Andry Rakotonirainy ◽  
Jonny Kuo ◽  
Michael G. Lenné
Keyword(s):  
Mobile Phone ◽  
Real World ◽  
Simulation Experiment ◽  
Driving Simulator ◽  
Reaction Times ◽  
Driving Simulation ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Driver Performance ◽  
Vehicle Technology

Objective The study aims to investigate the potential of using HUD (head-up display) as an approach for drivers to engage in non–driving-related tasks (NDRTs) during automated driving, and examine the impacts on driver state and take-over performance in comparison to the traditional mobile phone. Background Advances in automated vehicle technology have the potential to relieve drivers from driving tasks so that they can engage in NDRTs freely. However, drivers will still need to take-over control under certain circumstances. Method A driving simulation experiment was conducted using an Advanced Driving Simulator and real-world driving videos. Forty-six participants completed three drives in three display conditions, respectively (HUD, mobile phone and baseline without NDRT). The HUD was integrated with the vehicle in displaying NDRTs while the mobile phone was not. Drivers’ visual (e.g. gaze, blink) and physiological (e.g. ECG, EDA) data were collected to measure driver state. Two take-over reaction times (hand and foot) were used to measure take-over performance. Results The HUD significantly shortened the take-over reaction times compared to the mobile phone condition. Compared to the baseline condition, drivers in the HUD condition also experienced lower cognitive workload and physiological arousal. Drivers’ take-over reaction times were significantly correlated with their visual and electrodermal activities during automated driving prior to the take-over request. Conclusion HUDs can improve driver performance and lower workload when used as an NDRT interface. Application The study sheds light on a promising approach for drivers to engage in NDRTs in future AVs.

scholarly journals Identification of Driving Safety Profiles in Vehicle to Vehicle Communication System Based on Vehicle OBD Information

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 194
Author(s):  
Hussein Ali Ameen ◽  
Abd Kadir Mahamad ◽  
Sharifah Saon ◽  
Rami Qays Malik ◽  
Zahraa Hashim Kareem ◽  
...  
Keyword(s):  
Liquid Crystal Display ◽  
Driving Behavior ◽  
Low Noise ◽  
Light Signal ◽  
Classification Model ◽  
Driver Model ◽  
Driving Safety ◽  
Vehicle Speed ◽  
Driver Assistance Systems ◽  
Safe Driving

Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant benefit in identifying drivers who engage in unsafe driving practices to enhance road safety. The proposed method uses continuously logged driving data to collect vehicle operation information, including vehicle speed, engine revolutions per minute (RPM), throttle position, and calculated engine load via the on-board diagnostics (OBD) interface. Then the proposed method makes use of severity stratification of acceleration to create a driving behavior classification model to determine whether the current driving behavior belongs to safe driving or not. The safe driving behavior is characterized by an acceleration value that ranges from about ±2 m/s2. The risk of collision starts from ±4 m/s2, which represents in this study the aggressive drivers. By measuring the in-vehicle accelerations, it is possible to categorize the driving behavior into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems. When the vehicle is in a risk situation, the system based on nRF24L01 + power amplifier/low noise amplifier PA/LNA, global positioning system GPS, and OBD-II passes a signal to the driver using a dedicated liquid-crystal display LCD and light signal. Experimental results show the correctness of the proposed driving behavior analysis method can achieve an average of 90% accuracy rate in various driving scenarios.

Researches on Adaptive Cruise Control system: A state of the art review

2021 ◽  
pp. 095440702110192
Author(s):  
Liangyao Yu ◽  
Ruyue Wang
Keyword(s):  
Adaptive Cruise Control ◽  
Driving Safety ◽  
Cruise Control ◽  
Driver Assistance Systems ◽  
Human Machine Interaction ◽  
System A ◽  
Cruise Control System ◽  
Preceding Vehicle ◽  
Energy Economy ◽  
Machine Interaction

Adaptive Cruise Control (ACC) is one of Advanced Driver Assistance Systems (ADAS) which takes over vehicle longitudinal control under necessary driving scenarios. Vehicle in ACC mode automatically adjusts speed to follow the preceding vehicle based on evaluation of the surrounding traffic. ACC reduces drivers’ workload as well as improves driving safety, energy economy, and traffic flow. This article provides a comprehensive review of the researches on ACC. Firstly, an overview of ACC controller and applied control theories are introduced. Their principles and performances are discussed. Secondly, several application cases of ACC control algorithms are presented. Then validation work including simulation, Hardware-in-the-Loop (HiL) test and on-road experiment is descripted to provide ideas for testing ACC systems for different aims and fidelities. In addition, studies on human-machine interaction are also summarized in this review to provide insights on development of ACC from the perspective of users. At last, challenges and potential directions in this field is discussed, including consideration of vehicle dynamics properties, contradiction between algorithm performance and computation as well as integration of ACC to other intelligent functions on vehicles.

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