Evaluation of Drivers’ Attention Level by a Driving Simulator

2012 ◽  
Author(s):  
Riccardo Bartolozzi ◽  
Francesco Frendo
Keyword(s):  
Vehicle Dynamics ◽  
Driving Simulator ◽  
Automatic Transmission ◽  
Point Of View ◽  
Steering Wheel ◽  
Data Logging ◽  
Feedback Systems ◽  
Specific Test ◽  
Xpc Target ◽  
Attention Level

Diagnosis systems for evaluating driver’s attention level have become very important in the last years and have gained an increasing attention from automotive manufacturers; indeed, traffic crashes represent worldwide one of the main public health problems and many accidents are demonstrated to be due to drivers’ hypovilance. In this work a driving simulator and specific test tools were developed to validate the possibility of monitoring the drivers’ attention level and capability. The driving simulator is equipped with a fixed cockpit and a single front screen. The cockpit reproduces the man-machine interface of a typical medium class car with automatic transmission, i.e. the driver interacts with the simulator by means of the throttle and brake pedals and the steering wheel. The pedals are endowed with passive feedback systems, whereas an electric motor applies an active feedback torque on the steering wheel. From the software point of view, the simulator is managed by four PCs connected by a LAN. Two of them are dedicated to the simulation of vehicle dynamics and for data logging, while the other two run the graphical scenario software. This includes a motorway area connected to an urban area with an autonomous traffic. The vehicle model was built with a block architecture in the Matlab/Simulink environment and is run in real-time by using the xPC Target toolbox. A specific block, consisting of an in-house developed 14 d.o.f. model, was set up to represents vehicle dynamics. The driving simulator is currently employed in experimental campaigns acquiring about 60 signals of driver’s input and vehicle quantities. In order to evaluate the drivers’ attention level, two specific tests were developed, which aim at measuring the drivers’ reaction time and ability in sudden events such as a brake manoeuvre or a sudden lateral skid. In the paper the driving simulator hardware and software are presented. The tests procedures, which were developed to investigate the driver’s attention, are also discussed with reference to some tests.

Understanding Drivers’ Steering Behavior: Chain And One-Time Corrections

2018 ◽  
Vol 62 (1) ◽  
pp. 1858-1862
Author(s):  
Azadeh DinparastDjadid ◽  
John D. Lee ◽  
Chris Schwarz ◽  
Timothy L. Brown ◽  
Pujitha Gunaratne
Keyword(s):  
Vehicle Dynamics ◽  
Driving Simulator ◽  
Situational Factors ◽  
Steering Wheel ◽  
Behavior Chain ◽  
Control Actions ◽  
Underlying Causes ◽  
Simulator Study

Drivers’ steering adjustments can be categorized into one-time and chain corrections. One-time corrections lead to no further steering corrections for a minimum of one second, while chain corrections have at least two consecutive steering actions. Chain corrections represent a novel indicator of steering instability. Evolving vehicle dynamics along with drivers’ state and situational factors can cause these different correction types. In a driving simulator study, drivers’ experienced different roadway widths with and without distraction. The results show that higher steering wheel angle values at the beginning or end of a correction lead to chain corrections and the duration of these corrections tends to be shorter than adjustments not leading to chain corrections. Exploring the underlying causes of different corrections can guide efforts to model drivers’ control actions in recovering from distractions and in taking over control during automation failures.

scholarly journals Effects of Upper Extremity Immobilization and Use of a Spinner Knob on Vehicle Steering

Hand ◽  
2016 ◽  
Vol 12 (6) ◽  
pp. 597-605 ◽  
Author(s):  
Lyle T. Jackson ◽  
Matthew C. Crisler ◽  
Stephanie L. Tanner ◽  
Johnell O. Brooks ◽  
Kyle J. Jeray
Keyword(s):  
Reaction Time ◽  
Crossover Trial ◽  
Driving Simulator ◽  
Automatic Transmission ◽  
Driving Ability ◽  
Difficulty Level ◽  
Steering Wheel ◽  
Improve Accuracy ◽  
Negative Effect ◽  
Impact Driving

Background: A person’s ability to safely drive while immobilized is not well defined. Steering ability with a spinner knob during immobilization is unknown. The goal of this study is to further clarify the effect of immobilization on steering reaction time and accuracy with and without a steering wheel spinner knob. Methods: Twenty participants were enrolled in this crossover trial using a driving simulator with an automatic transmission. Five conditions were tested in a counterbalanced order. Steering reaction time and accuracy (number of errors on a dynamic steering task at 2 difficulty levels) were measured. Participants were allowed to steer with the immobilized extremity. Results: No significant differences in reaction time were observed between any conditions. Both immobilized conditions and difficulty level of the steering task led to diminished accuracy compared with controls, resulting in significantly more errors. The use of a spinner knob significantly improved the accuracy for the condition with the sugar-tong splint during the easier steering task, but this improvement was not observed in the harder steering task. There were no differences between conditions based on gender or observed use of the immobilized arm. Conclusions: Immobilization had a negative effect on steering accuracy for both the wrist splint and the sugar-tong splint condition, which may negatively impact driving ability of immobilized patients. Immobilization, regardless of spinner knob use, did not significantly impact steering reaction time. The steering wheel spinner knob did not consistently improve accuracy, and further study is needed to determine its utility.

scholarly journals Lane Departure Warning Estimation Using Yaw Acceleration

2020 ◽  
Vol 11 (1) ◽  
pp. 102-111
Author(s):  
Em Poh Ping ◽  
J. Hossen ◽  
Wong Eng Kiong
Keyword(s):  
Vehicle Dynamics ◽  
Dynamic Responses ◽  
Dynamics Simulation ◽  
Steering Wheel ◽  
Mathematical Representation ◽  
Vehicle Speed ◽  
Lane Departure Warning ◽  
Model Based ◽  
Lane Departure ◽  
Proposed Model

AbstractLane departure collisions have contributed to the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Due to vision-based lane departure warning limitation from environmental conditions that affecting system performance, a model-based vehicle dynamics framework is proposed for estimating the lane departure event by using vehicle dynamics responses. The model-based vehicle dynamics framework mainly consists of a mathematical representation of 9-degree of freedom system, which permitted to pitch, roll, and yaw as well as to move in lateral and longitudinal directions with each tire allowed to rotate on its axle axis. The proposed model-based vehicle dynamics framework is created with a ride model, Calspan tire model, handling model, slip angle, and longitudinal slip subsystems. The vehicle speed and steering wheel angle datasets are used as the input in vehicle dynamics simulation for predicting lane departure event. Among the simulated vehicle dynamic responses, the yaw acceleration response is observed to provide earlier insight in predicting the future lane departure event compared to other vehicle dynamics responses. The proposed model-based vehicle dynamics framework had shown the effectiveness in estimating lane departure using steering wheel angle and vehicle speed inputs.

scholarly journals Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle Automation

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 13
Author(s):  
Thierry Bellet ◽  
Aurélie Banet ◽  
Marie Petiot ◽  
Bertrand Richard ◽  
Joshua Quick
Keyword(s):  
Artificial Intelligence ◽  
Adaptive Management ◽  
Driving Simulator ◽  
End Users ◽  
Point Of View ◽  
Human Centered Design ◽  
Perceived Utility ◽  
New Type ◽  
Future Potential ◽  
Vehicle Automation

This article is about the Human-Centered Design (HCD), development and evaluation of an Artificial Intelligence (AI) algorithm aiming to support an adaptive management of Human-Machine Transition (HMT) between car drivers and vehicle automation. The general principle of this algorithm is to monitor (1) the drivers’ behaviors and (2) the situational criticality to manage in real time the Human-Machine Interactions (HMI). This Human-Centered AI (HCAI) approach was designed from real drivers’ needs, difficulties and errors observed at the wheel of an instrumented car. Then, the HCAI algorithm was integrated into demonstrators of Advanced Driving Aid Systems (ADAS) implemented on a driving simulator (dedicated to highway driving or to urban intersection crossing). Finally, user tests were carried out to support their evaluation from the end-users point of view. Thirty participants were invited to practically experience these ADAS supported by the HCAI algorithm. To increase the scope of this evaluation, driving simulator experiments were implemented among three groups of 10 participants, corresponding to three highly contrasted profiles of end-users, having respectively a positive, neutral or reluctant attitude towards vehicle automation. After having introduced the research context and presented the HCAI algorithm designed to contextually manage HMT with vehicle automation, the main results collected among these three profiles of future potential end users are presented. In brief, main findings confirm the efficiency and the effectiveness of the HCAI algorithm, its benefits regarding drivers’ satisfaction, and the high levels of acceptance, perceived utility, usability and attractiveness of this new type of “adaptive vehicle automation”.

Study on Driver and Vehicle System With Lane-Tracking Control System Using Steering Torque Input

2001 ◽  
Author(s):  
Masao Nagai ◽  
Hidehisa Yoshida ◽  
Kiyotaka Shitamitsu ◽  
Hiroshi Mouri
Keyword(s):  
Tracking Control ◽  
High Speed ◽  
Driving Simulator ◽  
Tracking System ◽  
Torque Control ◽  
Steering Wheel ◽  
Control Input ◽  
Steering Angle ◽  
Lane Tracking ◽  
Steering Torque

Abstract Although the vast majority of lane-tracking control methods rely on the steering wheel angle as the control input, a few studies have treated methods using the steering torque as the input. When operating vehicles especially at high speed, drivers typically do not grip the steering wheel tightly to prevent the angle of the steering wheel from veering off course. This study proposes a new steering assist system for a driver not with the steering angle but the steering torque as the input and clarifies the characteristics and relative advantages of the two approaches. Then using a newly developed driving simulator, characteristics of human drivers and the lane-tracking system based on the steering torque control are investigated.

Development of a Driving Simulator for Virtual Evaluation of Vehicle Dynamics

2010 ◽  
Author(s):  
A. M. Sharaf
Keyword(s):  
Performance Evaluation ◽  
Vehicle Dynamics ◽  
Driving Simulator ◽  
Field Testing ◽  
Vehicle Performance ◽  
Vehicle Simulation ◽  
Virtual Platform ◽  
The Cost ◽  
Vehicle Testing ◽  
Simulation Package

This paper delineates the conceptual algorithms of a driving simulator which is intended for vehicle performance evaluation and to act as a virtual platform for research studies and therefore eliminates the cost and dangerous of field testing. A virtual proving ground for vehicle testing has been devised through which virtual handling, traction and ride tests can be performed. A fully instrumented simulator cabin combining the driver and the vehicle simulation package is developed. Different vehicle configurations are simulated during typical sever manoeuvres which reflects the robustness and fidelity of the devised simulator.

scholarly journals The Effects of Vehicle Level Of Automation and Warning Type on Responses to Vehicle Hacking

2019 ◽  
Vol 63 (1) ◽  
pp. 1977-1977
Author(s):  
Wyatt McManus ◽  
Jing Chen
Keyword(s):  
Driving Simulator ◽  
Blind Spot ◽  
Steering Wheel ◽  
Automated Driving ◽  
Impact Surface ◽  
Level Of Automation ◽  
Surface Transportation ◽  
Levels Of Automation ◽  
Automation Level ◽  
Different Levels

Modern surface transportation vehicles often include different levels of automation. Higher automation levels have the potential to impact surface transportation in unforeseen ways. For example, connected vehicles with higher levels of automation are at a higher risk for hacking attempts, because automated driving assistance systems often rely on onboard sensors and internet connectivity (Amoozadeh et al., 2015). As the automation level of vehicle control rises, it is necessary to examine the effect different levels of automation have on the driver-vehicle interactions. While research into the effect of automation level on driver-vehicle interactions is growing, research into how automation level affects driver’s responses to vehicle hacking attempts is very limited. In addition, auditory warnings have been shown to effectively attract a driver’s attention while performing a driving task, which is often visually demanding (Baldwin, 2011; Petermeijer, Doubek, & de Winter, 2017). An auditory warning can be either speech-based containing sematic information (e.g., “car in blind spot”) or non-sematic (e.g., a tone, or an earcon), which can influence driver behaviors differently (Sabic, Mishler, Chen, & Hu, 2017). The purpose of the current study was to examine the effect of level of automation and warning type on driver responses to novel critical events, using vehicle hacking attempts as a concrete example, in a driving simulator. The current study compared how level of automation (manual vs. automated) and warning type (non-semantic vs. semantic) affected drivers’ responses to a vehicle hacking attempt using time to collision (TTC) values, maximum steering wheel angle, number of successful responses, and other measures of response. A full factorial between-subjects design with the two factors made four conditions (Manual Semantic, Manual Non-Semantic, Automated Semantic, and Automated Non-Semantic). Seventy-two participants recruited using SONA ( odupsychology.sona-systems.com ) completed two simulated drives to school in a driving simulator. The first drive ended with the participant safely arriving at school. A two-second warning was presented to the participants three quarters of the way through the second drive and was immediately followed by a simulated vehicle hacking attempt. The warning either stated “Danger, hacking attempt incoming” in the semantic conditions or was a 500 Hz sine tone in the non-semantic conditions. The hacking attempt lasted five seconds before simulating a crash into a vehicle and ending the simulation if no intervention by the driver occurred. Our results revealed no significant effect of level of automation or warning type on TTC or successful response rate. However, there was a significant effect of level of automation on maximum steering wheel angle. This is a measure of response quality (Shen & Neyens, 2017), such that manual drivers had safer responses to the hacking attempt with smaller maximum steering wheel angles. In addition, an effect of warning type that approached significance was also found for maximum steering wheel angle such that participants who received a semantic warning had more severe and dangerous responses to the hacking attempt. The TTC and successful response results from the current experiment do not match those in the previous literature. The null results were potentially due to the warning implementation time and the complexity of the vehicle hacking attempt. In contrast, the maximum steering wheel angle results indicated that level of automation and warning type affected the safety and severity of the participants’ responses to the vehicle hacking attempt. This suggests that both factors may influence responses to hacking attempts in some capacity. Further research will be required to determine if level of automation and warning type affect participants ability to safely respond to vehicle hacking attempts. Acknowledgments. We are grateful to Scott Mishler for his assistance with STISIM programming and Faye Wakefield, Hannah Smith, and Pettie Perkins for their assistance in data collection.

scholarly journals MATHEMATICAL MODELLING OF THE UNMANNED AERIAL VEHICLE DYNAMICS

2018 ◽  
pp. 37-44
Author(s):  
V. Y. Stepanov
Keyword(s):  
Mathematical Model ◽  
Mathematical Modelling ◽  
Unmanned Aerial Vehicle ◽  
Vehicle Dynamics ◽  
Point Of View ◽  
Dynamics Analysis ◽  
Aerial Vehicle ◽  
Main Components ◽  
Controlled Object

The article gives a classification of the main components of unmanned aerial vehicle (UAV) systems, gives the areas in which the application of UAVs is actual in practice today. Further, the UAV is considered in more detail from the point of view of its flight dynamics analysis, the equation necessary for creating a mathematical model, as well as the model of an ordinary dynamic system as a non-stationary nonlinear controlled object, is given. Next, a description of the developed software for modeling and a description of program algorithm are given. Finally, a conclusion describes the necessary directions for further scientific researches.

scholarly journals Railway Transition Curves: A Review of the State-of-the-Art and Future Research

2020 ◽  
Vol 5 (5) ◽  
pp. 43 ◽  
Author(s):  
Tanita Fossli Brustad ◽  
Rune Dalmo
Keyword(s):  
Vehicle Dynamics ◽  
State Of The Art ◽  
Evaluation Criteria ◽  
Research Topic ◽  
Point Of View ◽  
Future Research ◽  
Geometric Continuity ◽  
Railway Industry ◽  
Literature Reviews ◽  
Transition Curves

Transition curves are a useful tool for lateral alignment of railway segments. Their design is important to ensure safe and comfortable travel for passengers and cargo. Well designed transition curves can lead to reduced wear of tracks and vehicles, which is beneficial from a maintenance point of view. Extensive studies have been performed through decades to find transition curves that can replace existing railway segments for the purpose of enhancing certain properties. Those studies seek to form curves that satisfy desired evaluation criteria, which are often connected to geometric continuity between the curve segments, and vehicle dynamics, to secure a smooth ride. This research topic is still ongoing and active at present. Recent results and findings are in line with the developments on the topic of vehicle dynamics and within the railway industry. For this reason it is appropriate to collect and discuss the latest work, since there are no up-to-date detailed literature reviews available. This paper explores the present state-of-the-art of railway transition curves, and identifies some of the research challenges and future research opportunities in the field.

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