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Lane departures represent an important cause of road crashes. The objective of the present study was to assess the effects of an auditory Lane Departure Warning System (LDWS) for partial and full lane departures (onset manipulation) combined with missed warnings (reliability manipulation: 100% reliable, 83% reliable and 66% reliable) on drivers' performances and acceptance. Several studies indicate that LDWS improves drivers' performances during lane departure episodes. However, little is known about the effects of the warning onset and reliability of LDWS. Results of studies which looked at forward collision warning systems show that early warnings tend to improve drivers' performances and receive a better trust judgement from the drivers when compared to later warnings. These studies also suggest that reliable assistances are more effective and trusted than unreliable ones. In the present study, lane departures were brought about by means of a distraction task whilst drivers simulated driving in a fixed-base simulator with or without an auditory LDWS. Results revealed steering behaviors improvements with LDWS. More effective recovery maneuvers were found with partial lane departure warnings than with full lane departure warnings and assistance unreliability did not impair significantly drivers' behaviors. Regarding missed lane departure episodes, drivers were found to react later and spend more time out of the driving lane when compared to properly warned lane departures, as if driving without assistance. Subjectively, LDWS did not reduce mental workload and partial lane departure warnings were judged more trustworthy than full lane departure ones. Data suggests the use of partial lane departure warnings when designing LDWS and that even unreliable LDWS may draw benefits compared to no assistance.

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Objective Automated driving is becoming a reality, and such technology raises new concerns about human–machine interaction on road. This paper aims to investigate factors influencing trust calibration and evolution over time. Background Numerous studies showed trust was a determinant in automation use and misuse, particularly in the automated driving context. Method Sixty-one drivers participated in an experiment aiming to better understand the influence of initial level of trust (Trustful vs. Distrustful) on drivers’ behaviors and trust calibration during two sessions of simulated automated driving. The automated driving style was manipulated as positive (smooth) or negative (abrupt) to investigate human–machine early interactions. Trust was assessed over time through questionnaires. Drivers’ visual behaviors and take-over performances during an unplanned take-over request were also investigated. Results Results showed an increase of trust over time, for both Trustful and Distrustful drivers regardless the automated driving style. Trust was also found to fluctuate over time depending on the specific events handled by the automated vehicle. Take-over performances were not influenced by the initial level of trust nor automated driving style. Conclusion Trust in automated driving increases rapidly when drivers’ experience such a system. Initial level of trust seems to be crucial in further trust calibration and modulate the effect of automation performance. Long-term trust evolutions suggest that experience modify drivers’ mental model about automated driving systems. Application In the automated driving context, trust calibration is a decisive question to guide such systems’ proper utilization, and road safety.

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