Why Do I Have to Drive Now? Post Hoc Explanations of Takeover Requests.

OBJECTIVE: It was investigated whether providing an explanation for a takeover request in automated driving influences trust in automation and acceptance. BACKGROUND: Takeover requests will be recurring events in conditionally automated driving that could undermine trust as well as acceptance and, therefore, the successful introduction of automated vehicles. METHOD: Forty participants were equally assigned to either an experimental group provided with an explanation of the reason for a takeover request or a control group without explanations. In a simulator drive, both groups experienced three takeover scenarios that varied in the obviousness of their causation. Participants rated their acceptance before and after the drive and rated their trust before and after each takeover situation. RESULTS: All participants rated acceptance on the same high level before and after the drive, independent of the condition. The control group's trust ratings remained unchanged by takeover requests in all situations, but the experimental group showed decreased trust after experiencing a takeover caused by roadwork. Participants provided with explanation felt more strongly that they had understood the system and the reasons for the takeovers. CONCLUSION: A takeover request did not lower trust or acceptance. Providing an explanation for a takeover request had no impact on trust or acceptance but increased the perceived understanding of the system. APPLICATION: The results provide insights into users' perception of automated vehicles, takeover situations, and a fundament for future interface design for automated vehicles.

Introduction matters: Manipulating trust in automation and reliance in automated driving.

Trust in automation is a key determinant for the adoption of automated systems and their appropriate use. Therefore, it constitutes an essential research area for the introduction of automated vehicles to road traffic. In this study, we investigated the influence of trust promoting (Trust promoted group) and trust lowering (Trust lowered group) introductory information on reported trust, reliance behavior and take-over performance. Forty participants encountered three situations in a 17-min highway drive in a conditionally automated vehicle (SAE Level 3). Situation 1 and Situation 3 were non-critical situations where a take-over was optional. Situation 2 represented a critical situation where a take-over was necessary to avoid a collision. A non-driving-related task (NDRT) was presented between the situations to record the allocation of visual attention. Participants reporting a higher trust level spent less time looking at the road or instrument cluster and more time looking at the NDRT. The manipulation of introductory information resulted in medium differences in reported trust and influenced participants' reliance behavior. Participants of the Trust promoted group looked less at the road or instrument cluster and more at the NDRT. The odds of participants of the Trust promoted group to overrule the automated driving system in the non-critical situations were 3.65 times (Situation 1) to 5 times (Situation 3) higher. In Situation 2, the Trust promoted group's mean take-over time was extended by 1154 ms and the mean minimum time-to-collision was 933 ms shorter. Six participants from the Trust promoted group compared to no participant of the Trust lowered group collided with the obstacle. The results demonstrate that the individual trust level influences how much drivers monitor the environment while performing an NDRT. Introductory information influences this trust level, reliance on an automated driving system, and if a critical take-over situation can be successfully solved.

Taking Over Control From Highly Automated Vehicles in Complex Traffic Situations: The Role of Traffic Density.

OBJECTIVE: The aim of this study was to quantify the impact of traffic density and verbal tasks on takeover performance in highly automated driving. BACKGROUND: In highly automated vehicles, the driver has to occasionally take over vehicle control when approaching system limits. To ensure safety, the ability of the driver to regain control of the driving task under various driving situations and different driver states needs to be quantified. METHODS: Seventy-two participants experienced takeover situations requiring an evasive maneuver on a three-lane highway with varying traffic density (zero, 10, and 20 vehicles per kilometer). In a between-subjects design, half of the participants were engaged in a verbal 20-Questions Task, representing speaking on the phone while driving in a highly automated vehicle. RESULTS: The presence of traffic in takeover situations led to longer takeover times and worse takeover quality in the form of shorter time to collision and more collisions. The 20-Questions Task did not influence takeover time but seemed to have minor effects on the takeover quality. CONCLUSIONS: For the design and evaluation of human-machine interaction in takeover situations of highly automated vehicles, the traffic state seems to play a major role, compared to the driver state, manipulated by the 20-Questions Task. APPLICATION: The present results can be used by developers of highly automated systems to appropriately design human-machine interfaces and to assess the driver's time budget for regaining control.