Measuring Drivers’ Physiological Response to Different Vehicle Controllers in Highly Automated Driving (HAD): Opportunities for Establishing Real-Time Values of Driver Discomfort

Radhakrishnan, Vishnu; Merat, Natasha; Louw, Tyron; Lenné, Michael G.; Paschalidis, Evangelos; Hajiseyedjavadi, Foroogh; Wei, Chongfeng; Boer, Erwin R.

doi:10.3390/info11080390

Cited by 15 publications

(14 citation statements)

References 38 publications

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“…The last paper "Measuring Drivers' Physiological Response to Different Vehicle Controllers in Highly Automated Driving (HAD): Opportunities for Establishing Real-Time Values of Driver Discomfort" by Radhakrishnan, Merat, Louw, Lenné, Romano, Paschalidis, Hajiseyedjavadi, Wei and Boer [21] investigates how driver discomfort was influenced by different types of automated vehicle (AV) controllers, compared to manual driving, and whether this response changed in different road environments, using heart-rate variability and electrodermal activity. The drivers were subjected to manual driving and four AV controllers: two modelled to depict "human-like" driving behavior, one conventional lane-keeping assist controller, and a replay of their own manual drive.…”

Section: Evaluating the Influence Of Driver State Driver Availabilitmentioning

confidence: 99%

Editorial for Special Issue: Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles

et al. 2020

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Today, OEMs and suppliers can rely on commonly agreed and standardized test and evaluation methods for in-vehicle human–machine interfaces (HMIs). These have traditionally focused on the context of manually driven vehicles and put the evaluation of minimizing distraction effects and enhancing usability at their core (e.g., AAM guidelines or NHTSA visual-manual distraction guidelines). However, advances in automated driving systems (ADS) have already begun to change the driver’s role from actively driving the vehicle to monitoring the driving situation and being ready to intervene in partially automated driving (SAE L2). Higher levels of vehicle automation will likely only require the driver to act as a fallback ready user in case of system limits and malfunctions (SAE L3) or could even act without any fallback within their operational design domain (SAE L4). During the same trip, different levels of automation might be available to the driver (e.g., L2 in urban environments, L3 on highways). These developments require new test and evaluation methods for ADS, as available test methods cannot be easily transferred and adapted. The shift towards higher levels of vehicle automation has also moved the discussion towards the interaction between automated and non-automated road users using exterior HMIs. This Special Issue includes theoretical papers a well as empirical studies that deal with these new challenges by proposing new and innovative test methods in the evaluation of ADS HMIs in different areas.

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Section: Evaluating the Influence Of Driver State Driver Availabilitmentioning

confidence: 99%

Editorial for Special Issue: Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles

et al. 2020

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“…Recent driving simulator experiments, using wearable electrodermal activity (EDA) and electrocardiogram (ECG) sensors, have shown a promising alternative to overcome eye-trackers major drawbacks and towards the development of multimodal DSM systems [34]- [37]. For example, simply detecting if the driver is taking a nap with the seat reclined, awake and listening to music while looking through the window, or fully immersed in a videogame may imply radically different preparation to take-over strategies to get the driver back in the loop.…”

Section: Introductionmentioning

confidence: 99%

“…Both ECG and EDA have proven their validity in detecting drivers' stress or mental workload in naturalistic driving [44] and driving simulators [37], [45], sleepiness [46], and discomfort [34]- [36]. In particular, increasing stress levels have been found under complex driving conditions [44], [47], possibly due to increased perceived risk.…”

Section: Introductionmentioning

confidence: 99%

Physiological Measures of Risk Perception in Highly Automated Driving

Perelló-March

Burns

Birrell

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Highly automated driving will likely result in drivers being out-of-the-loop during specific scenarios and engaging in a wide range of non-driving related tasks. Manifesting in lower levels of risk perception to emerging events, and thus affect drivers' availability to take-over manual control in safety-critical scenarios. In this empirical research, we measured drivers' (N = 20) risk perception with cardiac and skin conductance indicators through a series of high-fidelity, simulated highly automated driving scenarios. By manipulating the presence of surrounding traffic and changing driving conditions as long-term risk modulators, and including a driving hazard event as a short-term risk modulator, we hypothesised that an increase in risk perception would induce greater physiological arousal. Our results demonstrate that heart rate variability features are superior at capturing arousal variations from these long-term, low to moderate risk scenarios. In contrast, skin conductance responses are more sensitive to rapidly evolving situations associated with moderate to high risk. Based on this research, future driver state monitoring systems should adopt multiple physiological measures to capture changes in the long and short term, modulation of risk perception. This will enable enhanced perception of driver readiness and improved availability to safely deal with take-over events when requested by an automated vehicle.

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“…Users' perception of an AV's driving style is known to be influenced by both objective and subjective factors. For example, road furniture and geometry are known to influence ratings of safety and comfort (Hajiseyedjavadi et al, submitted) and physiological response (Beggiato et al, 2019;Radhakrishnan et al, 2020), while a number of studies have shown a correlation between personality traits such as Sensation Seeking (Arnett, 1994) and preferred driving style. For example, in manual driving; drivers with high sensation seeking scores are found to drive in a riskier and more aggressive manner and at higher speeds, while low sensation seekers have higher tendency to drive more carefully (Louw et al, 2019;Taubman-Ben-Ari et al, 2004;Zuckerman & Neeb, 1980).…”

Section: Introductionmentioning

confidence: 99%

Drivers’ Evaluation of Different Automated Driving Styles: Is It both Comfortable and Natural?

Peng¹,

Merat²,

Romano³

et al. 2021

Preprint

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Objective: This study investigated users’ subjective evaluation of three highly automated driving styles, in terms of comfort and naturalness, when negotiating a UK road in a high-fidelity, motion-based, driving simulator. Background: Comfort and naturalness are thought to play an important role in contributing to users’ acceptance and trust of automated vehicles (AVs), although not much is understood about the types of driving style which are considered comfortable or natural. Method: A driving simulator study, simulating roads with different road geometries and speed limits, was conducted. Twenty-four participants experienced three highly automated driving styles, two of which were recordings from human drivers, and the other was based on a machine learning (ML) algorithm, termed Defensive, Aggressive, and Turner respectively. Participants evaluated comfort or naturalness of each driving style, for each road segment, and completed a Sensation Seeking (SS) questionnaire, which assessed their risk-taking propensity. Results: Participants regarded human-like driving styles as more comfortable and natural, compared with the less human-like, ML-based, driving controller. However, between the two human-like controllers, only the Defensive style was considered comfortable, especially for the more challenging road environments. Differences in preference for controller by driver trait were also observed, with the Aggressive driving style evaluated as more natural by the high sensation seekers. Conclusion: Participants were able to distinguish between human- and machine-like AV controllers. A range of psychological concepts must be considered for the subjective evaluation of controllers. Application: Knowing how different driver groups evaluate automated vehicle controllers is important to design more acceptable systems in the future.

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Measuring Drivers’ Physiological Response to Different Vehicle Controllers in Highly Automated Driving (HAD): Opportunities for Establishing Real-Time Values of Driver Discomfort

Cited by 15 publications

References 38 publications

Editorial for Special Issue: Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles

Editorial for Special Issue: Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles

Physiological Measures of Risk Perception in Highly Automated Driving

Drivers’ Evaluation of Different Automated Driving Styles: Is It both Comfortable and Natural?

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