41 citations · 117 across the 31 of their papers we have counts for
32 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…
GENNAV: Polygon Mask Generation for Generalized Referring Navigable Regions
Kei Katsumata, Yui Iioka, Naoki Hosomi +3
We focus on the task of identifying the location of target regions from a natural language instruction and a front camera image captured by a mobility. This task is challenging bec…
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…
Optimal Driver Warning Generation in Dynamic Driving Environment
Chenran Li, Aolin Xu, Enna Sachdeva +2
The driver warning system that alerts the human driver about potential risks during driving is a key feature of an advanced driver assistance system. Existing driver warning techno…
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…
How is the Pilot Doing: VTOL Pilot Workload Estimation by Multimodal Machine Learning on Psycho-physiological Signals
Jong Hoon Park, Lawrence Chen, Ian Higgins +11
Vertical take-off and landing (VTOL) aircraft do not require a prolonged runway, thus allowing them to land almost anywhere. In recent years, their flexibility has made them popula…
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…
ViCor: Bridging Visual Understanding and Commonsense Reasoning with Large Language Models
Kaiwen Zhou, Kwonjoon Lee, Teruhisa Misu +1
In our work, we explore the synergistic capabilities of pre-trained vision-and-language models (VLMs) and large language models (LLMs) on visual commonsense reasoning (VCR) problem…
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…
Driving Anomaly Detection Using Conditional Generative Adversarial Network
Yuning Qiu, Teruhisa Misu, Carlos Busso
Anomaly driving detection is an important problem in advanced driver assistance systems (ADAS). It is important to identify potential hazard scenarios as early as possible to avoid…
Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction
Lingfeng Sun, Chen Tang, Yaru Niu +5
Motion forecasting in highly interactive scenarios is a challenging problem in autonomous driving. In such scenarios, we need to accurately predict the joint behavior of interactin…
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…
Explore the Potential Performance of Vision-and-Language Navigation Model: a Snapshot Ensemble Method
Wenda Qin, Teruhisa Misu, Derry Wijaya
Vision-and-Language Navigation (VLN) is a challenging task in the field of artificial intelligence. Although massive progress has been made in this task over the past few years att…
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'…