163 citations · 338 across the 22 of their papers we have counts for
23 papers
From Process Loss to Assembly Bonus: Human-Grounded Diagnosis of Multi-Agent LLM Collaboration
Ala N. Tak, Teruhisa Misu, Kumar Akash +3
LLM agents are increasingly used for collaborative problem solving and human-group simulation. This makes outcome-only evaluation insufficient: if LLM groups are used as models of…
Adaptive Driving Style for SAE Level-2 Driving Automation: Minimizing Preference Mismatch
Kumar Akash, Zhaobo Zheng, Teruhisa Misu +4
Driving style is a key factor in the comfort and acceptance of automated vehicle (AV) features. In SAE Level-2 automation, where the driver must supervise the system and remain rea…
Too Many Specialists: Emergent Inefficiencies and Bottlenecks for Multi-agent Ad-hoc Collaboration
Benjamin Panny, Shashank Mehrotra, Zahra Zahedi +2
Computational models of collaboration without prior coordination often overlook how heterogeneous agent traits and complex task structures jointly produce systemic bottlenecks, ine…
Strategic Shaping of Human Prosociality: A Latent-State POMDP Framework
Zahra Zahedi, Xinyue Hu, Shashank Mehrotra +2
We propose a decision-theoretic framework in which a robot strategically can shape inferred human's prosocial state during repeated interactions. Modeling the human's prosociality…
Exploring The Impact Of Proactive Generative AI Agent Roles In Time-Sensitive Collaborative Problem-Solving Tasks
Anirban Mukhopadhyay, Kevin Salubre, Hifza Javed +2
Collaborative problem-solving under time pressure is common but difficult, as teams must generate ideas quickly, coordinate actions, and track progress. Generative AI offers new op…
Self-Supervised Learning-Based Multimodal Prediction on Prosocial Behavior Intentions
Abinay Reddy Naini, Zhaobo K. Zheng, Teruhisa Misu +1
Human state detection and behavior prediction have seen significant advancements with the rise of machine learning and multimodal sensing technologies. However, predicting prosocia…
Toward Informed AV Decision-Making: Computational Model of Well-being and Trust in Mobility
Zahra Zahedi, Shashank Mehrotra, Teruhisa Misu +1
For future human-autonomous vehicle (AV) interactions to be effective and smooth, human-aware systems that analyze and align human needs with automation decisions are essential. Ac…
More than just a Tool: People's Perception and Acceptance of Prosocial Delivery Robots as Fellow Road Users
Vivienne Bihe Chi, Elise Ulwelling, Kevin Salubre +3
Service robots are increasingly deployed in public spaces, performing functional tasks such as making deliveries. To better integrate them into our social environment and enhance t…
Can we enhance prosocial behavior? Using post-ride feedback to improve micromobility interactions
Sidney T. Scott-Sharoni, Shashank Mehrotra, Kevin Salubre +3
Micromobility devices, such as e-scooters and delivery robots, hold promise for eco-friendly and cost-effective alternatives for future urban transportation. However, their lack of…
Should I Help a Delivery Robot? Cultivating Prosocial Norms through Observations
Vivienne Bihe Chi, Shashank Mehrotra, Teruhisa Misu +1
We propose leveraging prosocial observations to cultivate new social norms to encourage prosocial behaviors toward delivery robots. With an online experiment, we quantitatively ass…
DualTake: Predicting Takeovers across Mobilities for Future Personalized Mobility Services
Zhaobo Zheng, Kumar Akash, Teruhisa Misu
A hybrid society is expected to emerge in the near future, with different mobilities interacting together, including cars, micro-mobilities, pedestrians, and robots. People may uti…
Beyond Empirical Windowing: An Attention-Based Approach for Trust Prediction in Autonomous Vehicles
Minxue Niu, Zhaobo Zheng, Kumar Akash +1
Humans' internal states play a key role in human-machine interaction, leading to the rise of human state estimation as a prominent field. Compared to swift state changes such as su…
Wellbeing in Future Mobility: Toward AV Policy Design to Increase Wellbeing through Interactions
Shashank Mehrotra, Zahra Zahedi, Teruhisa Misu +1
Recent advances in Automated vehicle (AV) technology and micromobility devices promise a transformational change in the future of mobility usage. These advances also pose challenge…
Trust in Shared Automated Vehicles: Study on Two Mobility Platforms
Shashank Mehrotra, Jacob G Hunter, Matthew Konishi +6
The ever-increasing adoption of shared transportation modalities across the United States has the potential to fundamentally change the preferences and usage of different mobilitie…
The Interaction Gap: A Step Toward Understanding Trust in Autonomous Vehicles Between Encounters
Jacob G. Hunter, Matthew Konishi, Neera Jain +4
Shared autonomous vehicles (SAVs) will be introduced in greater numbers over the coming decade. Due to rapid advances in shared mobility and the slower development of fully autonom…
Identification of Adaptive Driving Style Preference through Implicit Inputs in SAE L2 Vehicles
Zhaobo K. Zheng, Kumar Akash, Teruhisa Misu +4
A key factor to optimal acceptance and comfort of automated vehicle features is the driving style. Mismatches between the automated and the driver preferred driving styles can make…
Effects of Augmented-Reality-Based Assisting Interfaces on Drivers' Object-wise Situational Awareness in Highly Autonomous Vehicles
Xiaofeng Gao, Xingwei Wu, Samson Ho +2
Although partially autonomous driving (AD) systems are already available in production vehicles, drivers are still required to maintain a sufficient level of situational awareness…
Learning Temporally and Semantically Consistent Unpaired Video-to-video Translation Through Pseudo-Supervision From Synthetic Optical Flow
Kaihong Wang, Kumar Akash, Teruhisa Misu
Unpaired video-to-video translation aims to translate videos between a source and a target domain without the need of paired training data, making it more feasible for real applica…
Improving Driver Situation Awareness Prediction using Human Visual Sensory and Memory Mechanism
Haibei Zhu, Teruhisa Misu, Sujitha Martin +2
Situation awareness (SA) is generally considered as the perception, understanding, and projection of objects' properties and positions. We believe if the system can sense drivers'…
Clustering Human Trust Dynamics for Customized Real-time Prediction
Jundi Liu, Kumar Akash, Teruhisa Misu +1
Trust calibration is necessary to ensure appropriate user acceptance in advanced automation technologies. A significant challenge to achieve trust calibration is to quantitatively…
Toward Adaptive Trust Calibration for Level 2 Driving Automation
Kumar Akash, Neera Jain, Teruhisa Misu
Properly calibrated human trust is essential for successful interaction between humans and automation. However, while human trust calibration can be improved by increased automatio…
Human Trust-based Feedback Control: Dynamically varying automation transparency to optimize human-machine interactions
Kumar Akash, Griffon McMahon, Tahira Reid +1
Human trust in automation plays an essential role in interactions between humans and automation. While a lack of trust can lead to a human's disuse of automation, over-trust can re…
A Classification Model for Sensing Human Trust in Machines Using EEG and GSR
Kumar Akash, Wan-Lin Hu, Neera Jain +1
Today, intelligent machines \emph{interact and collaborate} with humans in a way that demands a greater level of trust between human and machine. A first step towards building inte…