Interaction between AR Cue Types and Environmental Conditions in Autonomous Vehicles

Abstract

As one of autonomous vehicles, conditional autonomous vehicles is expected to become popular in the near future. Conditional autonomous vehicles can send a take-over request (TOR) to a driver, and if they are immersed in non-driving-related tasks (NDRT), they will struggle to accommodate this request. Previous studies have shown that providing augmented reality (AR) information on traffic situations (status cues) or driver actions (command cues) can improve TOR performance. However, we are not aware of any studies comparing the types of AR cues (state versus command cues) and their interactions with environmental factors. Therefore, the current study investigated this and evaluated the TOR performance of 42 drivers. We used a 2 (environments: day and night) $\times$ 4 (AR cue types: without, status, command, and combined cues) mixed-subject experimental design, and dependent measures included driving, cognitive, and NDRT performances. The results suggest that overall driving and cognitive performance were significantly improved by the command AR cue. In contrast, the status AR cue improved the TOR performance in nighttime environments. The performance of AR cues can vary depending on environmental factors, and AR cue designs for autonomous vehicles should consider this interaction for successful collaboration between drivers and vehicles.