Aggressive driving often triggers anger and retaliatory behaviors, posing threats to traffic safety. This paper proposes an AI-driven apology mechanism based on an Augmented Reality Head-Up Display (AR-HUD), which delivers immediate apologies on behalf of offending drivers during traffic conflicts and repairs damaged social relations through prosocial lies. We conducted a 2 (scenario risk: high vs. low) × 5 (apology depth) mixed-design experiment (N = 40) to evaluate its effectiveness. Results show that AI apologies enhanced positive emotions and forgiveness intentions while reducing anger, with participants also perceiving psychological benefits. These effects were consistent across both high- and low-risk scenarios. Our findings offer a practical design pathway for human-AI emotional regulation in traffic contexts.

In autonomous driving vehicles, the heterogeneity between human and automation agents can cause conflicts in decision-making and behaviour due to the difference in perception of hazardous situations. Augmented Reality Human-Machine Interfaces (AR-HMI) provide an opportunity to support driving performance by enabling drivers to intuitively access shared perception and explanation of the automated vehicle. One possible approach to AR-HMI design is to simplify the information of driving tasks based on vehicle context understanding, although there is currently a lack of systematic understanding of how collaborative mechanisms or cognitive features contribute to AR-HMI information design. Therefore, this work develops an augmented reality cognitive interface design method for autonomous driving. It aims to identify novel collaborative interface information visualization and provide a common language and inspiration for the design space.

In the context of intelligent systems for human-vehicle collaboration, the fusion of information space, physical space, and user cognitive space has become a trend. This paper aims to address the challenges posed by information perception gaps and cognitive limitations experienced by drivers by leveraging Augmented Reality Head-up Displays (ie, AR-HUD) to compensate for perceptual deficiencies and enhance driver cognition. We introduce the innovative concept of the Human-Machine Team Situation Awareness (ie, TSA) loop model. Firstly, we analyze the cognitive characteristics of drivers and the spatiotemporal information elements within hazardous scenarios. Subsequently, AR-HUDs are employed to provide drivers with perceptual compensation and cognitive enhancement. Furthermore, we design AR-HUD interfaces for two representative scenarios. The results demonstrate that, with the support of AR-HUDs, the integration of dynamic interface elements proves to be more effective in compensating for perceptual deficiencies, and the inclusion of predictive information contributes to improved driving performance. Notably, in emergency situations, AR-HUDs play a crucial role in providing decision-enhancing information to drivers. The proposed theoretical framework offers opportunities for expanding the theoretical approaches and application domains of AR-HUDs.

Driver’s instinctive responses and skill-based behaviors enable them to react faster and better control their vehicle in dangerous situations. This study incorporated dynamic spatial information design (DSID) in an augmented reality head-up display (AR-HUD) under manual driving conditions. By integrating the skill, rule, and knowledge (SRK) taxonomy and situation awareness (SA) theory, our AR-HUD successfully evoked drivers’ instinctive responses and improved driving safety. First, we converted symbol and sign information processed at the knowledge-based and rule-based levels, respectively, into signal information processed at the skill-based level. Then we developed four AR-HUD interfaces with different dynamic designs for use in a hazardous scenario at an intersection. Finally, we investigated each design’s impact on drivers’ SA and driving performance. Experimental results demonstrated that our DSID enhanced drivers’ SA and accident-avoidance capabilities while reducing their cognitive workload. Among the four AR-HUD interfaces, the one that incorporated all three information elements under study (i.e., lateral warning, dynamic driving space, and speedometer) performed the best. This indicates that our proposed framework has potential applications in other similar dangerous driving scenarios, thus contributing to the development of safer and more efficient driving environments.