Kumar Akash

Zhaobo K. Zheng, Kumar Akash, Teruhisa Misu et al.

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 users take over more frequently or even disable the automation features. This work proposes identification of user driving style preference with multimodal signals, so the vehicle could match user preference in a continuous and automatic way. We conducted a driving simulator study with 36 participants and collected extensive multimodal data including behavioral, physiological, and situational data. This includes eye gaze, steering grip force, driving maneuvers, brake and throttle pedal inputs as well as foot distance from pedals, pupil diameter, galvanic skin response, heart rate, and situational drive context. Then, we built machine learning models to identify preferred driving styles, and confirmed that all modalities are important for the identification of user preference. This work paves the road for implicit adaptive driving styles on automated vehicles.

Xiaofeng Gao, Xingwei Wu, Samson Ho et al.

Although partially autonomous driving (AD) systems are already available in production vehicles, drivers are still required to maintain a sufficient level of situational awareness (SA) during driving. Previous studies have shown that providing information about the AD's capability using user interfaces can improve the driver's SA. However, displaying too much information increases the driver's workload and can distract or overwhelm the driver. Therefore, to design an efficient user interface (UI), it is necessary to understand its effect under different circumstances. In this paper, we focus on a UI based on augmented reality (AR), which can highlight potential hazards on the road. To understand the effect of highlighting on drivers' SA for objects with different types and locations under various traffic densities, we conducted an in-person experiment with 20 participants on a driving simulator. Our study results show that the effects of highlighting on drivers' SA varied by traffic densities, object locations and object types. We believe our study can provide guidance in selecting which object to highlight for the AR-based driver-assistance interface to optimize SA for drivers driving and monitoring partially autonomous vehicles.

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 applications. Unfortunately, the translated videos generally suffer from temporal and semantic inconsistency. To address this, many existing works adopt spatiotemporal consistency constraints incorporating temporal information based on motion estimation. However, the inaccuracies in the estimation of motion deteriorate the quality of the guidance towards spatiotemporal consistency, which leads to unstable translation. In this work, we propose a novel paradigm that regularizes the spatiotemporal consistency by synthesizing motions in input videos with the generated optical flow instead of estimating them. Therefore, the synthetic motion can be applied in the regularization paradigm to keep motions consistent across domains without the risk of errors in motion estimation. Thereafter, we utilize our unsupervised recycle and unsupervised spatial loss, guided by the pseudo-supervision provided by the synthetic optical flow, to accurately enforce spatiotemporal consistency in both domains. Experiments show that our method is versatile in various scenarios and achieves state-of-the-art performance in generating temporally and semantically consistent videos. Code is available at: https://github.com/wangkaihong/Unsup_Recycle_GAN/.

Benjamin Panny, Shashank Mehrotra, Zahra Zahedi et al.

Computational models of collaboration without prior coordination often overlook how heterogeneous agent traits and complex task structures jointly produce systemic bottlenecks, inefficiencies, and contribution inequalities. We address this by using an agent-based model of ad-hoc teamwork in a kitchen environment. Our model integrates diverse agent personas with tasks that combine serial and parallel dependencies. We identify a specialist's dilemma, where rigid role assertion generates system-level bottlenecks, amplifies workload inequality, and fosters fragmented, homophilous networks. We also find that team size and communication overhead interact with problem structure to generate diminishing returns and redundant collaboration. Linking micro-level behavior to macro-level outcomes provides insights into emergent collaboration and design principles for effective multi-agent teamwork.

Minxue Niu, Zhaobo Zheng, Kumar Akash et al.

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 surprise and irritation, modeling gradual states like trust and satisfaction are further challenged by label sparsity: long time-series signals are usually associated with a single label, making it difficult to identify the critical span of state shifts. Windowing has been one widely-used technique to enable localized analysis of long time-series data. However, the performance of downstream models can be sensitive to the window size, and determining the optimal window size demands domain expertise and extensive search. To address this challenge, we propose a Selective Windowing Attention Network (SWAN), which employs window prompts and masked attention transformation to enable the selection of attended intervals with flexible lengths. We evaluate SWAN on the task of trust prediction on a new multimodal driving simulation dataset. Experiments show that SWAN significantly outperforms an existing empirical window selection baseline and neural network baselines including CNN-LSTM and Transformer. Furthermore, it shows robustness across a wide span of windowing ranges, compared to the traditional windowing approach.

Abinay Reddy Naini, Zhaobo K. Zheng, Teruhisa Misu et al.

Human state detection and behavior prediction have seen significant advancements with the rise of machine learning and multimodal sensing technologies. However, predicting prosocial behavior intentions in mobility scenarios, such as helping others on the road, is an underexplored area. Current research faces a major limitation. There are no large, labeled datasets available for prosocial behavior, and small-scale datasets make it difficult to train deep-learning models effectively. To overcome this, we propose a self-supervised learning approach that harnesses multi-modal data from existing physiological and behavioral datasets. By pre-training our model on diverse tasks and fine-tuning it with a smaller, manually labeled prosocial behavior dataset, we significantly enhance its performance. This method addresses the data scarcity issue, providing a more effective benchmark for prosocial behavior prediction, and offering valuable insights for improving intelligent vehicle systems and human-machine interaction.

Zahra Zahedi, Shashank Mehrotra, Teruhisa Misu et al.

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. Achieving this requires systems that account for human cognitive states. We present a novel computational model in the form of a Dynamic Bayesian Network (DBN) that infers the cognitive states of both AV users and other road users, integrating this information into the AV's decision-making process. Specifically, our model captures the well-being of both an AV user and an interacting road user as cognitive states alongside trust. Our DBN models infer beliefs over the AV user's evolving well-being, trust, and intention states, as well as the possible well-being of other road users, based on observed interaction experiences. Using data collected from an interaction study, we refine the model parameters and empirically assess its performance. Finally, we extend our model into a causal inference model (CIM) framework for AV decision-making, enabling the AV to enhance user well-being and trust while balancing these factors with its own operational costs and the well-being of interacting road users. Our evaluation demonstrates the model's effectiveness in accurately predicting user's states and guiding informed, human-centered AV decisions.

Haibei Zhu, Teruhisa Misu, Sujitha Martin et al.

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' SA, it can appropriately provide warnings for objects that drivers are not aware of. To investigate drivers' awareness, in this study, a human-subject experiment of driving simulation was conducted for data collection. While a previous predictive model for drivers' situation awareness utilized drivers' gaze movement only, this work utilizes object properties, characteristics of human visual sensory and memory mechanism. As a result, the proposed driver SA prediction model achieves over 70% accuracy and outperforms the baselines.

Jundi Liu, Kumar Akash, Teruhisa Misu et al.

Trust calibration is necessary to ensure appropriate user acceptance in advanced automation technologies. A significant challenge to achieve trust calibration is to quantitatively estimate human trust in real-time. Although multiple trust models exist, these models have limited predictive performance partly due to individual differences in trust dynamics. A personalized model for each person can address this issue, but it requires a significant amount of data for each user. We present a methodology to develop customized model by clustering humans based on their trust dynamics. The clustering-based method addresses the individual differences in trust dynamics while requiring significantly less data than personalized model. We show that our clustering-based customized models not only outperform the general model based on entire population, but also outperform simple demographic factor-based customized models. Specifically, we propose that two models based on ``confident'' and ``skeptical'' group of participants, respectively, can represent the trust behavior of the population. The ``confident'' participants, as compared to the ``skeptical'' participants, have higher initial trust levels, lose trust slower when they encounter low reliability operations, and have higher trust levels during trust-repair after the low reliability operations. In summary, clustering-based customized models improve trust prediction performance for further trust calibration considerations.

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 automation transparency, too much transparency can overwhelm human workload. To address this tradeoff, we present a probabilistic framework using a partially observable Markov decision process (POMDP) for modeling the coupled trust-workload dynamics of human behavior in an action-automation context. We specifically consider hands-off Level 2 driving automation in a city environment involving multiple intersections where the human chooses whether or not to rely on the automation. We consider automation reliability, automation transparency, and scene complexity, along with human reliance and eye-gaze behavior, to model the dynamics of human trust and workload. We demonstrate that our model framework can appropriately vary automation transparency based on real-time human trust and workload belief estimates to achieve trust calibration.

Kumar Akash, Griffon McMahon, Tahira Reid et al.

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 result in a human trusting a faulty autonomous system which could have negative consequences for the human. Therefore, human trust should be calibrated to optimize human-machine interactions with respect to context-specific performance objectives. In this article, we present a probabilistic framework to model and calibrate a human's trust and workload dynamics during his/her interaction with an intelligent decision-aid system. This calibration is achieved by varying the automation's transparency---the amount and utility of information provided to the human. The parameterization of the model is conducted using behavioral data collected through human-subject experiments, and three feedback control policies are experimentally validated and compared against a non-adaptive decision-aid system. The results show that human-automation team performance can be optimized when the transparency is dynamically updated based on the proposed control policy. This framework is a first step toward widespread design and implementation of real-time adaptive automation for use in human-machine interactions.

Kumar Akash, Wan-Lin Hu, Neera Jain et al.

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 intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real-time. In this paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust sensor model based on the general feature set (i.e., a "general trust sensor model"). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.