Effects of Task-Induced Fatigue in Prolonged Conditional Automated Driving

Objective: The aim of this study was to investigate the effects of task-induced fatigue in prolonged conditional automated driving on takeover performance. Background: In conditional automated driving, the driver can engage in non–driving related tasks (NDRTs) and does not have to monitor the system and the driving environment. In the event that the system hits its limits, the human driver must regain control of the car. To ensure safety, adequate driver fallback performance is necessary. Effects of the drivers’ state and the engagement in NDRTs need to be investigated. Method: Seventy-three participants experienced prolonged conditional automated rides and simultaneously had to engage in either an activating quiz or a fatiguing monitoring task (between subjects). After 50 minutes, a takeover situation occurred, and participants had to regain control of the car. Results: Prolonged conditional automated driving and simultaneously engaging in NDRTs affected the driver’s state and the takeover performance of the participants. Takeover performance was impaired when participants had to deal with monotonous NDRTs. Conclusion: An engagement in monotonous monitoring tasks in conditional automated driving affects the driver’s state and takeover performance when it comes to takeover situations. Especially in prolonged automated driving, an adequate driver state seems to be necessary for safety reasons. Application: The results of this study demonstrate that engagement in monotonous NDRTs while driving conditionally automated may negatively affect takeover performance. A monitoring of the driver state and adapted assistance in a takeover situation seems to be a good opportunity to ensure safety.

[1]  André Charles,et al.  Fatigue, sleepiness, and performance in simulated versus real driving conditions. , 2005, Sleep.

[2]  Bryan Reimer,et al.  MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation , 2017 .

[3]  Edward M. Hitchcock,et al.  Active and passive fatigue in simulated driving: discriminating styles of workload regulation and their safety impacts. , 2013, Journal of experimental psychology. Applied.

[4]  D. Manzey.  Psychophysiologie mentaler Beanspruchung. , 1997 .

[5]  Christopher D. Wickens,et al.  The Transfer of Failure-Detection Skills between Monitoring and Controlling Dynamic Systems , 1982 .

[6]  Lisanne Bainbridge,et al.  Ironies of automation , 1982, Autom..

[7]  Sandy L. Zabell,et al.  The Subjective and the Objective , 2011 .

[8]  Raja Parasuraman,et al.  Humans and Automation: Use, Misuse, Disuse, Abuse , 1997, Hum. Factors.

[9]  T. Åkerstedt,et al.  Subjective and objective sleepiness in the active individual. , 1990, The International journal of neuroscience.

[10]  Michael Schrauf,et al.  EEG alpha spindle measures as indicators of driver fatigue under real traffic conditions , 2011, Clinical Neurophysiology.

[11]  E. Hasser,et al.  Arterial and cardiopulmonary reflexes in the regulation of the neurohumoral drive to the circulation. , 1985, Federation proceedings.

[12]  R. Kronauer,et al.  Dose-response relationship between sleep duration and human psychomotor vigilance and subjective alertness. , 1999, Sleep.

[13]  Carryl L. Baldwin,et al.  Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies , 2009 .

[14]  Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles , 2022 .

[15]  R J Fairbanks,et al.  RESEARCH ON VEHICLE-BASED DRIVER STATUS/PERFORMANCE MONITORING; DEVELOPMENT, VALIDATION, AND REFINEMENT OF ALGORITHMS FOR DETECTION OF DRIVER DROWSINESS. FINAL REPORT , 1994 .

[16]  Riccardo Bernardini,et al.  Stress Evaluation in Simulated Autonomous and Manual Driving through the Analysis of Skin Potential Response and Electrocardiogram Signals , 2020, Sensors.

[17]  Claire Marrison,et al.  Effects of Motivation and Cabin Configuration on Emergency Aircraft Evacuation Behavior and Rates of Egress , 1996 .

[18]  Catherine Neubauer,et al.  Fatigue and Voluntary Utilization of Automation in Simulated Driving , 2012, Hum. Factors.

[19]  Klaus Bengler,et al.  Highly automated driving with a decoupled steering wheel , 2014 .

[20]  Frederik Naujoks,et al.  Testing Scenarios for Human Factors Research in Level 3 Automated Vehicles , 2017 .

[21]  Melissa M. Mallis,et al.  MANAGING FATIGUE BY DROWSINESS DETECTION: CAN TECHNOLOGICAL PROMISES BE REALIZED? , 1998 .

[22]  Natasha Merat,et al.  Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions , 2013 .

[23]  Klaus Bengler,et al.  It's Out of Our Hands Now! Effects of Non-Driving Related Tasks During Highly Automated Driving on Drivers' Fatigue , 2017 .

[24]  Klaus Bengler,et al.  Taking Over Control From Highly Automated Vehicles in Complex Traffic Situations , 2016, Hum. Factors.

[25]  Natasha Merat,et al.  Highly Automated Driving, Secondary Task Performance, and Driver State , 2012, Hum. Factors.

[26]  Thomas A. Dingus,et al.  HUMAN FACTORS DESIGN ISSUES FOR CRASH AVOIDANCE SYSTEMS , 1998 .

[27]  Josef F. Krems,et al.  Prior Familiarization With Takeover Requests Affects Drivers’ Takeover Performance and Automation Trust , 2017, Hum. Factors.

[28]  Josef F. Krems,et al.  Effects of Take-Over Requests and Cultural Background on Automation Trust in Highly Automated Driving , 2017 .

[29]  D. Dinges,et al.  Managing Fatigue by Drowsiness Detection , 1998 .

[30]  Hamish Jamson,et al.  Drivers' Perception of and Response to Brake Failure , 2005 .

[31]  Nadja Schömig,et al.  The Interaction Between Highly Automated Driving and the Development of Drowsiness , 2015 .

[32]  R. Schleicher,et al.  Blinks and saccades as indicators of fatigue in sleepiness warners: looking tired? , 2022 .

[33]  N. Mackworth.  The Breakdown of Vigilance during Prolonged Visual Search 1 , 1948 .

[34]  Naohisa Hashimoto,et al.  THE APPLICATION OF RTK-GPS AND STEER-BY WIRE TECHNOLOGY TO THE AUTOMATIC DRIVING OF , 2006 .

[35]  Michael Schrauf,et al.  The short-term effect of verbally assessing drivers’ state on vigilance indices during monotonous daytime driving , 2011 .

[36]  T. Triggs,et al.  Time of day variations in driving performance. , 1997, Accident; analysis and prevention.

[37]  G. Matthews,et al.  Task-induced fatigue states and simulated driving performance , 2002, The Quarterly journal of experimental psychology. A, Human experimental psychology.