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 Effects of gender, age, experience, and practice on driver reaction and acceptance of traffic jam chauffeur systems

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Husam Muslim ◽  
Makoto Itoh ◽  
Cho Kiu Liang ◽  
Jacobo Antona-Makoshi ◽  
Nobuyuki Uchida
Keyword(s):  
Traffic Congestion ◽  
Simulation Experiment ◽  
Demographic Factors ◽  
Driving Simulation ◽  
Lane Change ◽  
Automated Driving ◽  
Left Hand ◽  
Traffic Jam ◽  
Automated Vehicle ◽  
The Individual

AbstractThis study conducted a driving simulation experiment to compare four automated driving systems (ADS) designs during lane change demanding traffic situations on highways while accounting for the drivers’ gender, age, experience, and practice. A lane-change maneuver was required when the automated vehicle approaches traffic congestion on the left-hand lane. ADS-1 can only reduce the speed to synchronize with the congestion. ADS-2 reduces the speed and issues an optional request to intervene, advising the driver to change lanes manually. ADS-3 offers to overtake the congestion autonomously if the driver approves it. ADS-4 overtakes the congestion autonomously without the driver’s approval. Results of drivers’ reaction, acceptance, and trust indicated that differences between ADS designs increase when considering the combined effect of drivers’ demographic factors more than the individual effect of each factor. However, the more ADS seems to have driver-like capacities, the more impact of demographic factors is expected. While preliminary, these findings may help us understand how ADS users’ behavior can differ based on the interaction between human demographic factors and system design.

scholarly journals Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jaehyun Jason So ◽  
Sungho Park ◽  
Jonghwa Kim ◽  
Jejin Park ◽  
Ilsoo Yun
Keyword(s):  
Driving Simulator ◽  
Road Traffic ◽  
Vehicle Control ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
The Road ◽  
Traffic Conditions ◽  
Vehicle System ◽  
Driver’S Characteristics

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

scholarly journals User Monitoring in Autonomous Driving System Using Gamified Task: A Case for vr/AR in-Car Gaming

2021 ◽  
Author(s):  
Joseph K. Muguro ◽  
Pringgo Widyo Laksono ◽  
Yuta Sasatake ◽  
Kojiro Matsushita ◽  
Minoru Sasaki
Keyword(s):  
Real World ◽  
Secondary Task ◽  
Autonomous Driving ◽  
Driving Simulation ◽  
Control Loop ◽  
Automated Driving ◽  
Driving System ◽  
The Road ◽  
Current Test ◽  
Autonomous Driving System

As Automated Driving Systems (ADS) technology gets assimilated into the market, the driver’s obligation will be changed to a supervisory role. A key point to consider is the driver’s engagement in the secondary task to maintain the driver/user in the control loop. The paper’s objective is to monitor driver engagement with a game and identify any impacts the task has on hazard recognition. We designed a driving simulation using Unity3D and incorporated three tasks: No-task, AR-Video, and AR-Game tasks. The driver engaged in an AR object interception game while monitoring the road for threatening road scenarios. From the results, there was less than 1 second difference between the means of gaming task (mean = 2.55s, std = 0.1002s) to no-task (mean = 2.55s, std = 0.1002s). Game scoring followed three profiles/phases: learning, saturation, and decline profile. From the profiles, it is possible to quantify/infer drivers’ engagement with the game task. The paper proposes alternative monitoring that has utility, i.e., entertaining the user. Further experiments AR-Game focusing on real-world car environment will be performed to confirm the performance following the recommendations derived from the current test.

Driver Vigilance in Automated Vehicles: Hazard Detection Failures Are a Matter of Time

2018 ◽  
Vol 60 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Eric T. Greenlee ◽  
Patricia R. DeLucia ◽  
David C. Newton
Keyword(s):  
Safety Concern ◽  
Reaction Times ◽  
Vigilance Task ◽  
Time On Task ◽  
Vigilance Decrement ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Hazard Detection ◽  
And Performance

Objective: The primary aim of the current study was to determine whether monitoring the roadway for hazards during automated driving results in a vigilance decrement. Background: Although automated vehicles are relatively novel, the nature of human-automation interaction within them has the classic hallmarks of a vigilance task. Drivers must maintain attention for prolonged periods of time to detect and respond to rare and unpredictable events, for example, roadway hazards that automation may be ill equipped to detect. Given the similarity with traditional vigilance tasks, we predicted that drivers of a simulated automated vehicle would demonstrate a vigilance decrement in hazard detection performance. Method: Participants “drove” a simulated automated vehicle for 40 minutes. During that time, their task was to monitor the roadway for roadway hazards. Results: As predicted, hazard detection rate declined precipitously, and reaction times slowed as the drive progressed. Further, subjective ratings of workload and task-related stress indicated that sustained monitoring is demanding and distressing and it is a challenge to maintain task engagement. Conclusion: Monitoring the roadway for potential hazards during automated driving results in workload, stress, and performance decrements similar to those observed in traditional vigilance tasks. Application: To the degree that vigilance is required of automated vehicle drivers, performance errors and associated safety risks are likely to occur as a function of time on task. Vigilance should be a focal safety concern in the development of vehicle automation.

A Bayesian Regression Analysis of the Effects of Alert Presence and Scenario Criticality on Automated Vehicle Takeover Performance

2021 ◽  
pp. 001872082110100
Author(s):  
Hananeh Alambeigi ◽  
Anthony D. McDonald
Keyword(s):  
Driving Simulator ◽  
Bayesian Regression ◽  
Minimum Time ◽  
Time To Collision ◽  
Automated Vehicle ◽  
Driver Performance ◽  
Average Impact ◽  
Fail Safe ◽  
The Impact ◽  
System Designs

Objective This study investigates the impact of silent and alerted failures on driver performance across two levels of scenario criticality during automated vehicle transitions of control. Background Recent analyses of automated vehicle crashes show that many crashes occur after a transition of control or a silent automation failure. A substantial amount of research has been dedicated to investigating the impact of various factors on drivers’ responses, but silent failures and their interactions with scenario criticality are understudied. Method A driving simulator study was conducted comparing scenario criticality, alert presence, and two driving scenarios. Bayesian regression models and Fisher’s exact tests were used to investigate the impact of alert and scenario criticality on takeover performance. Results The results show that silent failures increase takeover times and the intensity of posttakeover maximum accelerations and decrease the posttakeover minimum time-to-collision. While the predicted average impact of silent failures on takeover time was practically low, the effects on minimum time-to-collision and maximum accelerations were safety-significant. The analysis of posttakeover control interaction effects shows that the effect of alert presence differs by the scenario criticality Conclusion Although the impact of the absence of an alert on takeover performance was less than that of scenario criticality, silent failures seem to play a substantial role—by leading to an unsafe maneuver—in critical automated vehicle takeovers. Application Understanding the implications of silent failure on driver’s takeover performance can benefit the assessment of automated vehicles’ safety and provide guidance for fail-safe system designs.

scholarly journals Takeover performance evaluation using driving simulation: a systematic review and meta-analysis

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sónia Soares ◽  
António Lobo ◽  
Sara Ferreira ◽  
Liliana Cunha ◽  
António Couto
Keyword(s):  
Systematic Review ◽  
Driving Simulator ◽  
Time Budget ◽  
Meta Analysis ◽  
Road Transport ◽  
Driving Simulation ◽  
Automated Driving ◽  
Experimental Conditions ◽  
Secondary Tasks ◽  
Crash Rates

Abstract Introduction In a context of increasing automation of road transport, many researchers have been dedicated to analyse the risks and safety implications of resuming the manual control of a vehicle after a period of automated driving. This paper performs a systematic review about drivers’ performance during takeover manoeuvres in driving simulator, a tool that is widely used in the evaluation of automated systems to reproduce risky situations that would not be possible to test in real roads. Objectives The main objectives are to provide a framework for the main strategies, experimental conditions and results obtained by takeover research using driving simulation, as well as to find whether different approaches may lead to different outcomes. Methodology First, a literature search following the PRISMA statement guidelines and checklist resulted in 36 relevant papers, which were described in detail according to the type of scenarios and takeover events, drivers’ engagement in secondary tasks and the assessed takeover performance measures. Then, those papers were included in a meta-analysis combining PAM clustering and ANOVA techniques to find patterns among the experimental conditions and to determine if those patterns have influence on the observed takeover performance. Conclusions Less complex experiments without secondary task engagement and conducted in low-fidelity simulators are associated with lower takeover times and crash rates. The takeover time increases with the time budget of the first alert, which reduces the pressure for a driver’s quick intervention.

scholarly journals Effectiveness of Variable Message Signs on Driving Behavior Based on a Driving Simulation Experiment

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xuedong Yan ◽  
Jiawei Wu
Keyword(s):  
Traffic Safety ◽  
Road Network ◽  
Simulation Experiment ◽  
Driving Simulator ◽  
Driving Behavior ◽  
Driving Simulation ◽  
Traffic Information ◽  
Variable Message Signs ◽  
Information Format ◽  
Message Signs

Variable message signs (VMSs), as one of the important ITS devices, provide real-time traffic information of road network to drivers in order to improve route choice and relieve the traffic congestion. In this study, the effectiveness of VMS on driving behavior was tested based on a driving simulation experiment. A road network with three levels of VMS location to route-diverging intersection and three types of VMS information format was designed in a high fidelity driving simulator platform. Fifty-two subjects who were classified by driver age, gender, and vocation successfully completed this experiment. The experimental results showed that driver characteristics, VMS location, and information format profoundly influence driving behaviors. Based on the research findings, it is suggested that VMS would be positioned between 150 m and 200 m upstream of the diverging point to balance the VMS effects on traffic safety and operation and the graphic information VMS format is better than the format with text massage only.

scholarly journals How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models

2019 ◽  
Vol 62 (7) ◽  
pp. 1212-1229
Author(s):  
Giulio Bianchi Piccinini ◽  
Esko Lehtonen ◽  
Fabio Forcolin ◽  
Johan Engström ◽  
Deike Albers ◽  
...  
Keyword(s):  
Prediction Model ◽  
Computational Models ◽  
Driving Simulator ◽  
Reaction Times ◽  
Automated System ◽  
Cruise Control ◽  
Automated Driving ◽  
Driver Response ◽  
Driving Condition ◽  
Simulator Study

Objective This paper aims to describe and test novel computational driver models, predicting drivers’ brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC). Background Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving. Method Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers’ arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study. Results The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study. Conclusion Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data. Application Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving.

Assessing Drivers’ Trust of Automated Vehicle Driving Styles With a Two-Part Mixed Model of Intervention Tendency and Magnitude

2019 ◽  
pp. 001872081988036 ◽  
Author(s):  
John D. Lee ◽  
Shu-Yuan Liu ◽  
Joshua Domeyer ◽  
Azadeh DinparastDjadid
Keyword(s):  
Mixed Model ◽  
Driving Simulator ◽  
Statistical Technique ◽  
Brake Pedal ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Accelerator Pedal ◽  
Driving Style ◽  
Automated Vehicle ◽  
Stop Sign

Objective: This study examines how driving styles of fully automated vehicles affect drivers’ trust using a statistical technique—the two-part mixed model—that considers the frequency and magnitude of drivers’ interventions. Background: Adoption of fully automated vehicles depends on how people accept and trust them, and the vehicle’s driving style might have an important influence. Method: A driving simulator experiment exposed participants to a fully automated vehicle with three driving styles (aggressive, moderate, and conservative) across four intersection types (with and without a stop sign and with and without crossing path traffic). Drivers indicated their dissatisfaction with the automation by depressing the brake or accelerator pedals. A two-part mixed model examined how automation style, intersection type, and the distance between the automation’s driving style and the person’s driving style affected the frequency and magnitude of their pedal depression. Results: The conservative automated driving style increased the frequency and magnitude of accelerator pedal inputs; conversely, the aggressive style increased the frequency and magnitude of brake pedal inputs. The two-part mixed model showed a similar pattern for the factors influencing driver response, but the distance between driving styles affected how often the brake pedal was pressed, but it had little effect on how much it was pressed. Conclusion: Eliciting brake and accelerator pedal responses provides a temporally precise indicator of drivers’ trust of automated driving styles, and the two-part model considers both the discrete and continuous characteristics of this indicator. Application: We offer a measure and method for assessing driving styles.

scholarly journals Displays for Productive Non-Driving Related Tasks: Visual Behavior and Its Impact in Conditionally Automated Driving

2021 ◽  
Vol 5 (4) ◽  
pp. 21
Author(s):  
Clemens Schartmüller ◽  
Klemens Weigl ◽  
Andreas Löcken ◽  
Philipp Wintersberger ◽  
Marco Steinhauser ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Reaction Times ◽  
Driving Performance ◽  
Automated Driving ◽  
Auditory Condition ◽  
Safety Critical ◽  
The Road ◽  
Within Subjects ◽  
On The Road ◽  
Vehicle Automation

(1) Background: Primary driving tasks are increasingly being handled by vehicle automation so that support for non-driving related tasks (NDRTs) is becoming more and more important. In SAE L3 automation, vehicles can require the driver-passenger to take over driving controls, though. Interfaces for NDRTs must therefore guarantee safe operation and should also support productive work. (2) Method: We conducted a within-subjects driving simulator study (N=53) comparing Heads-Up Displays (HUDs) and Auditory Speech Displays (ASDs) for productive NDRT engagement. In this article, we assess the NDRT displays’ effectiveness by evaluating eye-tracking measures and setting them into relation to workload measures, self-ratings, and NDRT/take-over performance. (3) Results: Our data highlights substantially higher gaze dispersion but more extensive glances on the road center in the auditory condition than the HUD condition during automated driving. We further observed potentially safety-critical glance deviations from the road during take-overs after a HUD was used. These differences are reflected in self-ratings, workload indicators and take-over reaction times, but not in driving performance. (4) Conclusion: NDRT interfaces can influence visual attention even beyond their usage during automated driving. In particular, the HUD has resulted in safety-critical glances during manual driving after take-overs. We found this impacted workload and productivity but not driving performance.

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