Trust calibration through perceptual and predictive information of the external context in autonomous vehicle

作者：Qi Gao, Lehan Chen, Yanwei Shi, Yuxuan Luo, Mowei Shen, Zaifeng Gao · 发表于：Transportation Research Part F Traffic Psychology and Behaviour · 年份：2024 · DOI：10.1016/j.trf.2024.09.019 · 被引用次数：13 · 研究领域：Human-Automation Interaction and Safety、Autonomous Vehicle Technology and Safety、Healthcare Technology and Patient Monitoring

Maintaining an appropriate level of trust is critical for driving safety in autonomous vehicles. While enhancing the driver’s situation awareness (SA) of system information in autonomous driving is known to significantly promote trust calibration, it remains unclear whether enhancing the driver’s SA of the external context during driving contributes to this calibration. This study addresses this gap by improving SA of the external context during Level 3 (L3) driving automation across various driving environments. Driving contexts were manipulated using distinct road conditions containing low, medium, or high contextual risks. To enhance driver’s SA of the driving context, we redesigned the in-vehicle central control panel to display real-time perceptual and predictive information about the external driving context. We hypothesized that SA of driving contexts would facilitate trust calibration rather than merely enhancing trust, allowing trust to adjust to appropriate levels under different driving conditions. Experiment 1 examined the impact of perceptual information about the road, traffic infrastructure, and surrounding vehicles on drivers’ trust. We found that driver’s trust decreased with increased contextual risk only when the reconfigured panel was used, while the number of accidents was not affected. Experiment 2 investigated the effect of predictive information about the external context on drivers’ trust by marking safe and dangerous zones around driver’s vehicle wit...