Does automated driving affect time-to-collision judgments?

Lodinger, Natalie R.; DeLucia, Patricia R.

doi:10.1016/j.trf.2019.04.025

Cited by 9 publications

(9 citation statements)

References 34 publications

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“…Taking a look at the 36 selected papers, only eight of them reported crash figures in the conducted experiments. From those, Choi et al [4], De Winter et al [6], Lodinger and DeLucia [26], and Naujoks et al [31] reported zero crashes. The remaining studies obtained collision rates varying from 0 to 60%, depending on the time budget, the NDRT engagement and the type of NDRT, and gaze behaviour.…”

Section: Takeover Performance Measuresmentioning

confidence: 98%

“…Both types feature a real or mock-up car and immersive video projection, with the latter adding dynamic feedback capabilities. Five studies were conducted in low-fidelity simulators, consisting of a gaming steering wheel and pedals, regular monitors and, sometimes, a car seat [24,26,53,61,66]. One study combined experiments in low-and medium-fidelity simulators [6].…”

Section: Takeover Eventsmentioning

confidence: 99%

“…These factors not only impact the promptness of drivers' reaction to a TOR, consequently affecting takeover performance, but also impact the earlier decision to undertake an NDRT. In some studies, the authors opted to provide all the information about how the system works and its limitations and boundaries [6,7,26]. From another perspective, some studies aimed to analyse a more critical scenario and do not inform participants that automation would fail [27,41].…”

Section: Presence Of Tormentioning

confidence: 99%

“…For instance, the steering reaction was considered as a minimum turning of 2 degrees by Louw et al [28] and Wan and Wu [53]. Lodinger and de Lucia [26] considered a brake response as 2% of the maximum possible brake pressure, and Wan and Wu [53] and Zeeb et al [62] increased that limit to 10%. In the analysed studies, the mean takeover time was extracted as the smallest value between braking, steering, or system deactivation, depending on the available information.…”

Section: Takeover Performance Measuresmentioning

confidence: 99%

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Takeover performance evaluation using driving simulation: a systematic review and meta-analysis

Soares

Lobo

Ferreira

et al. 2021

Eur. Transp. Res. Rev.

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Introduction In a context of increasing automation of road transport, many researchers have been dedicated to analyse the risks and safety implications of resuming the manual control of a vehicle after a period of automated driving. This paper performs a systematic review about drivers’ performance during takeover manoeuvres in driving simulator, a tool that is widely used in the evaluation of automated systems to reproduce risky situations that would not be possible to test in real roads. Objectives The main objectives are to provide a framework for the main strategies, experimental conditions and results obtained by takeover research using driving simulation, as well as to find whether different approaches may lead to different outcomes. Methodology First, a literature search following the PRISMA statement guidelines and checklist resulted in 36 relevant papers, which were described in detail according to the type of scenarios and takeover events, drivers’ engagement in secondary tasks and the assessed takeover performance measures. Then, those papers were included in a meta-analysis combining PAM clustering and ANOVA techniques to find patterns among the experimental conditions and to determine if those patterns have influence on the observed takeover performance. Conclusions Less complex experiments without secondary task engagement and conducted in low-fidelity simulators are associated with lower takeover times and crash rates. The takeover time increases with the time budget of the first alert, which reduces the pressure for a driver’s quick intervention.

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Section: Takeover Performance Measuresmentioning

confidence: 98%

Section: Takeover Eventsmentioning

confidence: 99%

Section: Presence Of Tormentioning

confidence: 99%

Section: Takeover Performance Measuresmentioning

confidence: 99%

See 2 more Smart Citations

Takeover performance evaluation using driving simulation: a systematic review and meta-analysis

Soares

Lobo

Ferreira

et al. 2021

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…For instance, DeLucia [79] found that time-remaining judgments are also influenced by the size of a perceived object and its height in the field. Furthermore, the human-machine interaction mode also affected the estimation of τ. Lodinger and DeLucia [80] found that TTC judgments are more accurate and that brake reaction times are shorter during automated driving than during manual driving. Alvarez et al [77] found that TTC estimation was influenced by the chromaticity of the humanmachine interface of the approaching vehicle.…”

Section: Temporal Featuresmentioning

confidence: 99%

Analysis of visual risk perception model for braking control behaviour of human drivers: A literature review

Liu

Wang

Nacpil³

et al. 2022

IET Intelligent Trans Sys

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In the interest of driving safety, it is essential to understand driver risk assessment to develop human-centred automated driving systems. Visual perception plays a core role in observing, analysing, and predicting driving crash risks. Furthermore, comprehensive progress of visual perception has been achieved in the development of risk perception metrics. However, it is still of interest to understand driver control behaviours, which depend on visual perception. Therefore, the objective of this literature review is to assess the potential of coupling time-remaining cues for risk perception and braking control behaviour. First, conventional risk perception metrics are classified based on trajectory, contact, and state principles, along with new analysis metrics that have been derived from conventional ones. Then, the analysis metrics are discussed based on visual perception information. Next, driver risk assessment is summarised according to the environment, driver, and object characteristics. Moreover, braking behaviour models are compared in relation to visual perception variables, action affordances, and behaviour dynamics. The current review further discusses the possibility of applying driver behaviour modelling to perception, navigation, and spatial awareness in autonomous vehicles. This human-centred approach has the potential to improve interaction between drivers and automation.

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Human-Automation Interaction for Semi-Autonomous Driving: Risk Communication and Trust

Chen

Mishler²,

Long³

et al. 2022

Human-Automation Interaction

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Does automated driving affect time-to-collision judgments?

Cited by 9 publications

References 34 publications

Takeover performance evaluation using driving simulation: a systematic review and meta-analysis

Takeover performance evaluation using driving simulation: a systematic review and meta-analysis

Analysis of visual risk perception model for braking control behaviour of human drivers: A literature review

Human-Automation Interaction for Semi-Autonomous Driving: Risk Communication and Trust

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