The Sooner the Better: Drivers’ Reactions to Two-Step Take-Over Requests in Highly Automated Driving

2018 ◽  
Vol 62 (1) ◽  
pp. 1883-1887 ◽  
Author(s):  
Sandra Epple ◽  
Fabienne Roche ◽  
Stefan Brandenburg
Keyword(s):  
Human Factors ◽  
Situational Awareness ◽  
Driver Behavior ◽  
Reaction Times ◽  
Vehicle Control ◽  
Driving Behavior ◽  
Second Step ◽  
Automated Driving ◽  
One Step ◽  
Highly Automated Driving

Driving behavior after take-over requests (TORs) is one of the most popular subjects in human factors re-search on highly automated driving. Many studies utilized one-step TOR procedures to prompt drivers to resume vehicle control. The present paper examines driver behavior when experiencing a two-step TOR procedure in different modalities. A two-step TOR gives drivers a choice to resume vehicle controls be-tween a warning (first step) and an alarm (second step). Our findings indicate that a substantial number of drivers resumes vehicle controls after the second step, resulting in a higher number of crashes. More generally, criticality of the driving situation increases with increasing reaction times. Driving and interview data suggest that step two of the TOR should be presented earlier. Alternatively, a multi-step TOR could be used to increase drivers’ situational awareness. Auditory TORs are associated with shorter reaction times than visual-auditory TORs. Implications on TOR design are discussed.

scholarly journals Behavioral Changes to Repeated Takeovers in Highly Automated Driving: Effects of the Takeover-Request Design and the Nondriving-Related Task Modality

2018 ◽  
Vol 61 (5) ◽  
pp. 839-849 ◽  
Author(s):  
Fabienne Roche ◽  
Anna Somieski ◽  
Stefan Brandenburg
Keyword(s):  
Subjective Experience ◽  
Vehicle Control ◽  
Behavioral Changes ◽  
Behavior Changes ◽  
Automated Driving ◽  
The Road ◽  
Gaze Patterns ◽  
On The Road ◽  
Task Modality ◽  
Highly Automated Driving

Objective: We investigated drivers’ behavior and subjective experience when repeatedly taking over their vehicles’ control depending on the design of the takeover request (TOR) and the modality of the nondriving-related task (NDRT). Background: Previous research has shown that taking over vehicle control after highly automated driving provides several problems for drivers. There is evidence that the TOR design and the NDRT modality may influence takeover behavior and that driver behavior changes with more experience. Method: Forty participants were requested to resume control of their simulated vehicle six times. The TOR design (auditory or visual-auditory) and the NDRT modality (auditory or visual) were varied. Drivers’ takeover behavior, gaze patterns, and subjective workload were recorded and analyzed. Results: Results suggest that drivers change their behavior to the repeated experience of takeover situations. An auditory TOR leads to safer takeover behavior than a visual-auditory TOR. And with an auditory TOR, the takeover behavior improves with experience. Engaging in the visually demanding NDRT leads to fewer gazes on the road than the auditory NDRT. Participants’ fixation duration on the road decreased over the three takeovers with the visually demanding NDRT. Conclusions: The results imply that (a) drivers change their behavior to repeated takeovers, (b) auditory TOR designs might be preferable over visual-auditory TOR designs, and (c) auditory demanding NDRTs allow drivers to focus more on the driving scene. Application: The results of the present study can be used to design TORs and determine allowed NDRTs in highly automated driving.

Human factors perspectives on highly automated driving

2020 ◽  
Vol 83 (4) ◽  
pp. 285
Author(s):  
Jordan Navarro ◽  
Catherine Gabaude
Keyword(s):  
Human Factors ◽  
Automated Driving ◽  
Highly Automated Driving

scholarly journals Take-over expectation and criticality in Level 3 automated driving: a test track study on take-over behavior in semi-trucks

2020 ◽  
Vol 22 (4) ◽  
pp. 733-744
Author(s):  
Alexander Lotz ◽  
Nele Russwinkel ◽  
Enrico Wohlfarth
Keyword(s):  
High Speed ◽  
Driver Behavior ◽  
Reaction Times ◽  
Multiple Time ◽  
Automated Driving ◽  
Passenger Cars ◽  
Test Track ◽  
Driving Assistance ◽  
Level 3 ◽  
Critical Situations

Abstract With the introduction of advanced driving assistance systems managing longitudinal and lateral control, conditional automated driving is seemingly in near future of series vehicles. While take-over behavior in the passenger car context has been investigated intensively in recent years, publications on semi-trucks with professional drivers are sparse. The effects influencing expert drivers during take-overs in this context lack thorough investigation and are required to design systems that facilitate safe take-overs. While multiple findings seem to cohere in passenger cars and semi-trucks, these findings rely on simulated studies without taking environments as found in the real world into account. A test track study was conducted, simulating highway driving with 27 professional non-affiliated truck drivers. The participants drove an automated Level 3 semi-truck while a non-driving-related task was available. Multiple time critical take-over situations were initiated during the drives to investigate four main objectives regarding driver behavior. (1) With these results, comparison of reaction times and behavior can be drawn to previous simulator studies. The effect of situation criticality (2) and training (3) of take-over situations is investigated. (4) The influence of warning expectation on driver behavior is explored. Results obtained displayed very quick time to hands on steering and time to first reaction all under 2.4 s. Highly critical situations generate very quick reaction times M = 0.81 s, while the manipulation of expectancy yielded no significant variation in reaction times. These reaction times serve as a reference of what can be expected from drivers under optimal take-over conditions, with quick reactions at high speed in critical situations.

Familiar versus Unfamiliar Drivers on Curves: Naturalistic Data Study

2019 ◽  
Vol 2673 (6) ◽  
pp. 225-235 ◽  
Author(s):  
Michael P. Pratt ◽  
Srinivas R. Geedipally ◽  
Bahar Dadashova ◽  
Lingtao Wu ◽  
Mohammadali Shirazi
Keyword(s):  
Human Factors ◽  
Peripheral Vision ◽  
Driver Behavior ◽  
Reaction Times ◽  
Horizontal Curves ◽  
Speed Prediction ◽  
Naturalistic Driving ◽  
Design Consistency ◽  
Roadway Design ◽  
Rural Highway

Human factors studies have shown that route familiarity affects driver behavior in various ways. Specifically, when drivers become more familiar with a roadway, they pay less attention to signs, adopt higher speeds, cut curves more noticeably, and exhibit slower reaction times to stimuli in their peripheral vision. Numerous curve speed models have been developed for purposes such as predicting driver behavior, evaluating roadway design consistency, and setting curve advisory speeds. These models are typically calibrated using field data, which gives information about driver behavior in relation to speed and sometimes lane placement, but does not provide insights into the drivers themselves. The objective of this paper is to examine the differences between the speeds of familiar and unfamiliar drivers as they traverse curves. The authors identified four two-lane rural highway sections in the State of Indiana which include multiple horizontal curves, and queried the Second Strategic Highway Research Program (SHRP2) database to obtain roadway inventory and naturalistic driving data for traversals through these curves. The authors applied a curve speed prediction model from the literature to predict the speed at the curve midpoints and compared the predicted speeds with observed speeds. The results of the analysis confirm earlier findings that familiar drivers choose higher speeds through curves. The successful use of the SHRP2 database for this analysis of route familiarity shows that the database can facilitate similar efforts for a wider range of driver behavior and human factors issues.

Situational Awareness and Time to Takeover: Exploring an Alternative Method to Measure Engagement with High-Level Automation

2017 ◽  
Vol 61 (1) ◽  
pp. 1452-1456
Author(s):  
Hallie Clark ◽  
Anne Collins McLaughlin ◽  
Jing Feng
Keyword(s):  
Situational Awareness ◽  
Automated System ◽  
Vehicle Speed ◽  
Automated Driving ◽  
Lane Position ◽  
Vehicle Automation ◽  
High Level ◽  
Human Factors Research ◽  
Potential Use ◽  
Highly Automated Driving

Within human factors research of highly automated driving, in addition to examining the design of notifications of expected takeovers of vehicle control, understanding how drivers handle unexpected takeovers is of critical importance. Given a sufficient level of situational awareness has been shown as a premise for task performance in various domains, this study aims to establish a situational awareness measure in the context of highly automated driving using a simple platform for experimentation. In this study, we measured participants’ situational awareness when viewing a video of simulated automated system, and their time-to-takeover when vehicle automation failed unexpectedly (as indicated by an abnormal change of vehicle speed and lane position). The results verified the link between lower levels of situational awareness and longer time to respond to a takeover event, suggesting the potential use of the simple platform for experimentation and quick prototyping.

Drivers’ Individual Design Preferences of Takeover Requests in Highly Automated Driving

i-com ◽  
2019 ◽  
Vol 18 (2) ◽  
pp. 167-178 ◽  
Author(s):  
Stefan Brandenburg ◽  
Sandra Epple
Keyword(s):  
Interface Design ◽  
Automated Driving ◽  
Step Procedure ◽  
Detailed Evaluation ◽  
One Step ◽  
Machine Interface ◽  
Design Preferences ◽  
Individual Design ◽  
Practical Implications ◽  
Highly Automated Driving

Abstract Highly automated cars will be on the worlds’ roads within the next decade. In highly automated driving the vehicle’s lateral and longitudinal controls can be passed on from the driver to the vehicle and back again. The design of a vehicle’s take-over requests will largely determine the driver’s performance after taking back vehicle control. In the scope of this paper, potential drivers of highly automated cars were asked about their preferences regarding the human-machine interface design of take-over requests. Participants were asked to evaluate eight different take-over requests that differed with respect to (a) take-over request procedure (one-step or two-step procedure), (b) visual take-over request modality (text or text and pictogram), and (c) auditory take-over request modality (tone or speech). Results showed that participants preferred a two-step procedure using text and speech to communicate take-over requests. A subsequent conjoint analysis revealed that take-over requests ideally use speech output in a two-step procedure. Finally, a detailed evaluation showed that the best take-over request interface received significantly higher user experience ratings regarding product characteristics as well as users’ emotions and consequences of product use than the worst take-over request interface. Results are related to the background literature and practical implications are discussed.

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