Human Factor Studies in Evaluation of Automated Highway System Attributes

The goal of the Automated Highway System (AHS) is to blend engineering ingenuity and technology to produce a new level of transportation services. Human factors are difficult to integrate with AHS design because they represent a variety of training, experience, skills, and goals. Human factor considerations are essential for AHS design because humans will be involved in automated driving. For instance, drivers may be expected to instruct their vehicles to exit locations, input parameters such as speed and desired headway, or take control in some emergency situations. The tasks that human drivers will be expected to execute have not yet been fully defined. One human factor dilemma that AHS engineers might face is that if human drivers are not allowed to intervene in the vehicle control process during malfunction and emergency situations, they may be trapped in a system with high failure rates. This could result in public distrust and a lack of public will to deploy an AHS. However, if drivers are allowed to take control of their vehicles at will, some may intervene at inappropriate times, causing a potential system failure. A framework has been developed for evaluating human factor concerns for automated vehicle control. These concerns involve basic driving tasks: ( a) detection, ( b) recognition, ( c) situation analysis, ( d) decision making, and ( e) control response. An analytical process to determine the responsibilities of the human driver, vehicle, and AHS infrastructure for these driving tasks is presented.

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Human Factors and the Automated Highway System

Proceedings of the Human Factors Society Annual Meeting ◽

10.1518/107118192786749793 ◽

1992 ◽

Vol 36 (15) ◽

pp. 1064-1067 ◽

Cited By ~ 5

Author(s):

Elizabeth Alicandri ◽

M. Joseph Moyer

Keyword(s):

Human Factors ◽

Control Systems ◽

Vehicle Control ◽

Intelligent Vehicle ◽

Department Of Transportation ◽

Automated Vehicle ◽

Automated Highway ◽

Automated Vehicle Control ◽

Transportation Program ◽

Highway System

The Intelligent Vehicle-Highway System (IVHS) is an important and broad ranging Department of Transportation program to reduce congestion and increase safety on the nation's highway system. The Automated Highway System (AHS) represents the full realization of one IVHS subsystem, Automated Vehicle Control Systems. Efforts are underway to define and resolve critical human factors questions related to the AHS. As part of the process, human factors issues will be identified through development of hypothetical AHS scenarios. This requires a generic AHS scenario be presented, and affiliated human factors issues identified.

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Necessity of Automated Vehicle Control Customization: Experimental Results during Lane Changing

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118775871 ◽

2018 ◽

Vol 2672 (22) ◽

pp. 1-9

Author(s):

Naohisa Hashimoto ◽

Simon Thompson ◽

Shin Kato ◽

Ali Boyali ◽

Sadayuki Tsugawa

Keyword(s):

Vehicle Control ◽

Experimental Results ◽

Single Type ◽

Automated Driving ◽

Lane Changing ◽

Automated Vehicle ◽

Vehicle Trajectories ◽

Significant Difference ◽

The Subject ◽

Automated Vehicle Control

This study investigated the necessity of automated vehicle control customization for individual drivers via a lane-changing experiment involving 35 subjects and an automated minivan. The experiment consisted of two automated driving conditions: one in which the subject was unable to override vehicle controls, the other with the option to override when the subject felt it was necessary. The automated vehicle drove at a speed of 40 km/h along three kinds of planned paths for lane changing, generated by Bezier curves; the distance required for lane changing was varied to obtain the preferred path of each subject. Various data obtained during driving, including vehicle trajectories and steering angles produced by subjects were logged. After automated driving, a questionnaire was administered to each subject. The experimental data showed that there was a statistically significant difference between comfort when the vehicle drove along the subject’s preferred path, and when it drove along other paths. The results of the questionnaire indicated that 46% of the subjects preferred the planned path that most closely resembled their own. In addition, quantitative analysis of driving data found that approximately 69% of the subjects preferred an automated driving control that resembled their own. However, it was also observed that certain subjects were open to multiple types of automated vehicle control. The experimental results indicate that drivers will not necessarily accept a single type of automated vehicle control, therefore customization will be necessary to improve acceptance of automated driving.

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Automatic Vehicle Control in Emergency Situations: Technical and Human Factor Aspects

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)44189-9 ◽

1998 ◽

Vol 31 (2) ◽

pp. 153-159

Author(s):

Coda Alessandro ◽

Antonello Pier Claudio ◽

Damiani Sergio ◽

Peters Björn

Keyword(s):

Human Factor ◽

Vehicle Control ◽

Emergency Situations

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Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/0018720819829572 ◽

2019 ◽

Vol 61 (4) ◽

pp. 642-688 ◽

Cited By ~ 35

Author(s):

Anthony D. McDonald ◽

Hananeh Alambeigi ◽

Johan Engström ◽

Gustav Markkula ◽

Tobias Vogelpohl ◽

...

Keyword(s):

Time Budget ◽

Empirical Studies ◽

Vehicle Control ◽

Influential Factors ◽

Automated Driving ◽

Secondary Tasks ◽

Safety Issues ◽

Automated Vehicle ◽

Automated Vehicle Control ◽

The Impact

Objective: This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. Background: Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. Method: Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. Results: The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. Conclusion: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. Application: Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.

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Drivers use active gaze to monitor waypoints during automated driving

Scientific Reports ◽

10.1038/s41598-020-80126-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Callum Mole ◽

Jami Pekkanen ◽

William E. A. Sheppard ◽

Gustav Markkula ◽

Richard M. Wilkie

Keyword(s):

Visual Information ◽

Vehicle Control ◽

Automated System ◽

Manual Control ◽

Automated Driving ◽

Automated Vehicle ◽

Time Headway ◽

The Gaze ◽

Gaze Patterns ◽

Automated Vehicle Control

AbstractAutomated vehicles (AVs) will change the role of the driver, from actively controlling the vehicle to primarily monitoring it. Removing the driver from the control loop could fundamentally change the way that drivers sample visual information from the scene, and in particular, alter the gaze patterns generated when under AV control. To better understand how automation affects gaze patterns this experiment used tightly controlled experimental conditions with a series of transitions from ‘Manual’ control to ‘Automated’ vehicle control. Automated trials were produced using either a ‘Replay’ of the driver’s own steering trajectories or standard ‘Stock’ trials that were identical for all participants. Gaze patterns produced during Manual and Automated conditions were recorded and compared. Overall the gaze patterns across conditions were very similar, but detailed analysis shows that drivers looked slightly further ahead (increased gaze time headway) during Automation with only small differences between Stock and Replay trials. A novel mixture modelling method decomposed gaze patterns into two distinct categories and revealed that the gaze time headway increased during Automation. Further analyses revealed that while there was a general shift to look further ahead (and fixate the bend entry earlier) when under automated vehicle control, similar waypoint-tracking gaze patterns were produced during Manual driving and Automation. The consistency of gaze patterns across driving modes suggests that active-gaze models (developed for manual driving) might be useful for monitoring driver engagement during Automated driving, with deviations in gaze behaviour from what would be expected during manual control potentially indicating that a driver is not closely monitoring the automated system.

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Drivers use active gaze to monitor waypoints during automated driving

10.31234/osf.io/kfevj ◽

2020 ◽

Author(s):

Callum Mole ◽

Jami Pekkanen ◽

William Sheppard ◽

Gustav Markkula ◽

Richard Wilkie

Keyword(s):

Visual Information ◽

Vehicle Control ◽

Automated Driving ◽

Experimental Conditions ◽

Automated Vehicle ◽

Time Headway ◽

The Gaze ◽

Gaze Patterns ◽

Automated Vehicle Control

Automated Vehicles (AVs) will change the role of the driver, from actively controlling the vehicle to primarily monitoring it. Removing the driver from the control loop could fundamentally change the way that drivers sample visual information from the scene, and in particular, alter the gaze patterns generated when under AV control. To better understand how automation affects gaze patterns this experiment used tightly controlled experimental conditions with a series of transitions from `Manual' control to `Automated' vehicle control. Automated trials were produced either using either a `Replay' of the driver's own steering trajectories or standard `Stock' trials that were identical for all participants. Gaze patterns produced during Manual and Automated conditions were recorded and compared. The results show that drivers looked slightly further ahead (increased gaze time headway) during Automation with only small differences between Stock and Replay trials. A novel mixture modelling method decomposed gaze patterns into two distinct categories and revealed that the gaze time headway increased for both during Automation. Further analyses revealed that while there was a general shift to look further ahead (and fixate the bend entry earlier) when under automated vehicle control, similar waypoint-tracking gaze patterns were produced during Manual driving and Automation. The consistency of gaze patterns across driving modes suggests that active-gaze models (developed for manual driving) might be useful for monitoring driver engagement during Automated driving.

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