Multimodal warning design for take-over request in conditionally automated driving

Hanna Yun,Ji Hyun Yang

doi:10.1186/s12544-020-00427-5

Abstract

PurposeHumans are required to respond to a vehicle’s request to take-over anytime even when they are not responsible for monitoring driving environments in automated driving, e.g., a SAE level-3 vehicle. Thus, a safe and effective delivery of a take-over request from an automated vehicle to a human is critical for the successful commercialization of automated vehicles.MethodsIn the current study, a set of human-in-the-loop experiments was conducted to compare diverse warning combinations by applying visual, auditory, and haptic modalities under systematically classified take-over request scenarios in conditionally automated driving. Forty-one volunteers consisting of 16 females and 25 males participated in the study. Vehicle and human data on response to take-over request were collected in two take-over scenarios, i.e., a disabled vehicle on the road ahead and a highway exit.ResultsVisual-auditory-haptic modal combination showed the best performance in both human behavioral and physiological data and visual-auditory warning in vehicle data. Visual-auditory-haptic warning combination showed the best performance when considering all performance indices. Meanwhile, visual-only warning, which is considered as a basic modality in manual driving, performed the worst in the conditionally automated driving situation.ConclusionsThese findings imply that the warning design in automated vehicles must be clearly differentiated from that of conventional manual driving vehicles. Future work shall include a follow-up experiment to verify the study results and compare more diverse multimodal combinations.

Highlights

Automated driving can reduce traffic accidents caused by human errors, thereby resulting in environmental improvement through reduced traffic jam and offer freedom to users in non-driving activities when automated systems are active [9]
Automation levels of automated vehicles are defined based on diverse criteria; in particular, human–machine interaction (HMI) depends on each automated level
According to Society of Automotive Engineers (SAE) automation level 3, that is, Yun and Yang European Transport Research Review (2020) 12:34 level 3 [27], humans are not obligated to monitor traffic environments because these environments are managed by the automated system in the designated area

Summary

Introduction

Automated driving can reduce traffic accidents caused by human errors, thereby resulting in environmental improvement through reduced traffic jam and offer freedom to users in non-driving activities when automated systems are active [9]. When the automated system reaches its limits, the human drivers are asked to respond safely and timely Such an automated vehicle is called a “conditional driving automation” [27]. The take-over request (TOR) situations of an SAE level 3 automated vehicle can be highly disturbing and puzzling to human drivers because the drivers may be in an “out-of-the-loop” status and completely detached from driving under the automated driving mode; as such, they fail to recognize whether a TOR is urgent. They might be texting, reading, or watching a video. Providing an understandable and non-obscure TOR method is a significant factor in enhancing the safety of automated driving technology

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Results

Conclusion