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Debargha Dey

Publications and source records attributed to Debargha Dey.

7 recordsLinked to original sources

Rethinking External Communication of Autonomous Vehicles: Is the Field Converging, Diverging, or Stalling?

As autonomous vehicles enter public spaces, external human-machine interfaces are proposed to support communication with external road users. A decade of research has produced hundreds of studies and reviews, yet it remains unclear whether the field is converging on shared principles or diverging across approaches. We present a multi-dimensional analysis of 620 publications, complemented by industry deployments and regulatory documents, to track research evolution and identify convergence. The analysis reveals several field-level patterns. First, convergence on a safety-first core: simple visual cues that clarify intent. Second, sustained divergence in necessity and implementation. Third, a progressive filtering funnel: broad exploration in research and concepts narrows in deployment and is codified by regulation into a minimal set of permitted signals. These insights point to a shift in emphasis for future work, from producing new prototypes toward consolidating evidence, clarifying points of contention, and developing frameworks that can adapt across contexts.

cs.HC

eHMI for All -- Investigating the Effect of External Communication of Automated Vehicles on Pedestrians, Manual Drivers, and Cyclists in Virtual Reality

With automated vehicles (AVs), the absence of a human operator could necessitate external Human-Machine Interfaces (eHMIs) to communicate with other road users. Existing research primarily focuses on pedestrian-AV interactions, with limited attention given to other road users, such as cyclists and drivers of manually driven vehicles. So far, no studies have compared the effects of eHMIs across these three road user roles. Therefore, we conducted a within-subjects virtual reality experiment (N=40), evaluating the subjective and objective impact of an eHMI communicating the AV's intention to pedestrians, cyclists, and drivers under various levels of distraction (no distraction, visual noise, interference). eHMIs positively influenced safety perceptions, trust, perceived usefulness, and mental demand across all roles. While distraction and road user roles showed significant main effects, interaction effects were only observed in perceived usability. Thus, a unified eHMI design is effective, facilitating the standardization and broader adoption of eHMIs in diverse traffic.

cs.HC

Exploring the Impacts of Background Noise on Auditory Stimuli of Audio-Visual eHMIs for Hearing, Deaf, and Hard-of-Hearing People

External Human-Machine Interfaces (eHMIs) have been proposed to enhance communication between automated vehicles (AVs) and pedestrians, with growing interest in multi-modal designs such as audio-visual eHMIs. Just as poor lighting can impair visual cues, a loud background noise may mask the auditory stimuli. However, its effects within these systems have not been examined, and little is known about how pedestrians -- particularly Deaf and Hard-of-Hearing (DHH) people -- perceive different types of auditory stimuli. We conducted a virtual reality study (Hearing N=25, DHH N=11) to examine the effects of background noise (quiet and loud) on auditory stimuli (baseline, bell, speech) within an audio-visual eHMI. Results revealed that: (1) Crossing experiences of DHH pedestrians significantly differ from Hearing pedestrians. (2) Loud background noise adversely affects pedestrians' crossing experiences. (3) Providing an additional auditory eHMI (bell/speech) improves crossing experiences. We outlined four practical implications for future eHMI design and research.

cs.HC

Socially Adaptive Autonomous Vehicles: Effects of Contingent Driving Behavior on Drivers' Experiences

Social scientists have argued that autonomous vehicles (AVs) need to act as effective social agents; they have to respond implicitly to other drivers' behaviors as human drivers would. In this paper, we investigate how contingent driving behavior in AVs influences human drivers' experiences. We compared three algorithmic driving models: one trained on human driving data that responds to interactions (a familiar contingent behavior) and two artificial models that intend to either always-yield or never-yield regardless of how the interaction unfolds (non-contingent behaviors). Results show a statistically significant relationship between familiar contingent behavior and positive driver experiences, reducing stress while promoting the decisive interactions that mitigate driver hesitance. The direct relationship between familiar contingency and positive experience indicates that AVs should incorporate socially familiar driving patterns through contextually-adaptive algorithms to improve the chances of successful deployment and acceptance in mixed human-AV traffic environments.

cs.HC

Quo-Vadis Multi-Agent Automotive Research? Insights from a Participatory Workshop and Questionnaire

The transition to mixed-traffic environments that involve automated vehicles, manually operated vehicles, and vulnerable road users presents new challenges for human-centered automotive research. Despite this, most studies in the domain focus on single-agent interactions. This paper reports on a participatory workshop (N = 15) and a questionnaire (N = 19) conducted during the AutomotiveUI '24 conference to explore the state of multi-agent automotive research. The participants discussed methodological challenges and opportunities in real-world settings, simulations, and computational modeling. Key findings reveal that while the value of multi-agent approaches is widely recognized, practical and technical barriers hinder their implementation. The study highlights the need for interdisciplinary methods, better tools, and simulation environments that support scalable, realistic, and ethically informed multi-agent research.

cs.HC

Exploring Holistic HMI Design for Automated Vehicles: Insights from a Participatory Workshop to Bridge In-Vehicle and External Communication

Human-Machine Interfaces (HMIs) for automated vehicles (AVs) are typically divided into two categories: internal HMIs for interactions within the vehicle, and external HMIs for communication with other road users. In this work, we examine the prospects of bridging these two seemingly distinct domains. Through a participatory workshop with automotive user interface researchers and practitioners, we facilitated a critical exploration of holistic HMI design by having workshop participants collaboratively develop interaction scenarios involving AVs, in-vehicle users, and external road users. The discussion offers insights into the escalation of interface elements as an HMI design strategy, the direct interactions between different users, and an expanded understanding of holistic HMI design. This work reflects a collaborative effort to understand the practical aspects of this holistic design approach, offering new perspectives and encouraging further investigation into this underexplored aspect of automotive user interfaces.

cs.HC

Holistic HMI Design for Automated Vehicles: Bridging In-Vehicle and External Communication

As the field of automated vehicles (AVs) advances, it has become increasingly critical to develop human-machine interfaces (HMI) for both internal and external communication. Critical dialogue is emerging around the potential necessity for a holistic approach to HMI designs, which promotes the integration of both in-vehicle user and external road user perspectives. This approach aims to create a unified and coherent experience for different stakeholders interacting with AVs. This workshop seeks to bring together designers, engineers, researchers, and other stakeholders to delve into relevant use cases, exploring the potential advantages and challenges of this approach. The insights generated from this workshop aim to inform further design and research in the development of coherent HMIs for AVs, ultimately for more seamless integration of AVs into existing traffic.

cs.HC