Driving Aggressively or Conservatively? Investigating the Effects of Automated Vehicle Interaction Type and Road Event on Drivers' Trust and Preferred Driving Style.

OBJECTIVE: This study aimed to investigate the impact of automated vehicle (AV) interaction mode on drivers' trust and preferred driving styles in response to pedestrian- and traffic-related road events. BACKGROUND: The rising popularity of AVs highlights the need for a deeper understanding of the factors that influence trust in AV. Trust is a crucial element, particularly because current AVs are only partially automated and may require manual takeover; miscalibrated trust could have an adverse effect on safe driver-vehicle interaction. However, before attempting to calibrate trust, it is vital to comprehend the factors that contribute to trust in automation. METHODS: Thirty-six individuals participated in the experiment. Driving scenarios incorporated adaptive SAE Level 2 AV algorithms, driven by participants' event-based trust in AVs and preferences for AV driving styles. The study measured participants' trust, preferences, and the number of takeover behaviors. RESULTS: Higher levels of trust and preference for more aggressive AV driving styles were found in response to pedestrian-related events compared to traffic-related events. Furthermore, drivers preferred the trust-based adaptive mode and had fewer takeover behaviors than the preference-based adaptive and fixed modes. Lastly, participants with higher trust in AVs favored more aggressive driving styles and made fewer takeover attempts. CONCLUSION: Adaptive AV interaction modes that depend on real-time event-based trust and event types may represent a promising approach to human-automation interaction in vehicles. APPLICATION: Findings from this study can support future driver- and situation-aware AVs that can adapt their behavior for improved driver-vehicle interaction.

The future of mobility-as-a-service: trust transfer across automated mobilities, from road to sidewalk.

While trust in different types of automated vehicles has been a major focus for researchers and vehicle manufacturers, few studies have explored how people trust automated vehicles that are not cars, nor how their trust may transfer across different mobilities enabled with automation. To address this objective, a dual mobility study was designed to measure how trust in an automated vehicle with a familiar form factor-a car-compares to, and influences, trust in a novel automated vehicle-termed sidewalk mobility. A mixed-method approach involving both surveys and a semi-structured interview was used to characterize trust in these automated mobilities. Results found that the type of mobility had little to no effect on the different dimensions of trust that were studied, suggesting that trust can grow and evolve across different mobilities when the user is unfamiliar with a novel automated driving-enabled (AD-enabled) mobility. These results have important implications for the design of novel mobilities.

Augmented Reality Interface Design Approaches for Goal-directed and Stimulus-driven Driving Tasks.

The automotive industry is rapidly developing new in-vehicle technologies that can provide drivers with information to aid awareness and promote quicker response times. Particularly, vehicles with augmented reality (AR) graphics delivered via head-up displays (HUDs) are nearing mainstream commercial feasibility and will be widely implemented over the next decade. Though AR graphics have been shown to provide tangible benefits to drivers in scenarios like forward collision warnings and navigation, they also create many new perceptual and sensory issues for drivers. For some time now, designers have focused on increasing the realism and quality of virtual graphics delivered via HUDs, and recently have begun testing more advanced 3D HUD systems that deliver volumetric spatial information to drivers. However, the realization of volumetric graphics adds further complexity to the design and delivery of AR cues, and moreover, parameters in this new design space must be clearly and operationally defined and explored. In this work, we present two user studies that examine how driver performance and visual attention are affected when using fixed and animated AR HUD interface design approaches in driving scenarios that require top-down and bottom-up cognitive processing. Results demonstrate that animated design approaches can produce some driving gains (e.g., in goal-directed navigation tasks) but often come at the cost of response time and distance. Our discussion yields AR HUD design recommendations and challenges some of the existing assumptions of world-fixed conformal graphic approaches to design.

Driver Behavior and Performance with Augmented Reality Pedestrian Collision Warning: An Outdoor User Study.

This article investigates the effects of visual warning presentation methods on human performance in augmented reality (AR) driving. An experimental user study was conducted in a parking lot where participants drove a test vehicle while braking for any cross traffic with assistance from AR visual warnings presented on a monoscopic and volumetric head-up display (HUD). Results showed that monoscopic displays can be as effective as volumetric displays for human performance in AR braking tasks. The experiment also demonstrated the benefits of conformal graphics, which are tightly integrated into the real world, such as their ability to guide drivers' attention and their positive consequences on driver behavior and performance. These findings suggest that conformal graphics presented via monoscopic HUDs can enhance driver performance by leveraging the effectiveness of monocular depth cues. The proposed approaches and methods can be used and further developed by future researchers and practitioners to better understand driver performance in AR as well as inform usability evaluation of future automotive AR applications.