Kumar Akash

• Doctor of Philosophy
• Scientist at Honda Research Institute USA, Inc.

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...

Evaluating drivers’ situation awareness (SA) is important in the implementation of alert prioritization. This study investigates the relationship between driving performance measures (speed, acceleration and brake usage, steering wheel and lane deviation), pedestrian interaction (location, direction and motion), and driver SA. To achieve this, a c...

As societies transition to hybrid mobility systems, interactions between automated vehicles (AVs) and human users in public spaces become more complex, highlighting the critical role of prosocial behaviors. These behaviors are essential for the seamless operation of interdependent transportation networks, helping to address integration challenges o...

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 their adoption, we consider integrating social identities within delivery robots along with their functional identity. We conducted a virtual reality-based pilot study to...

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 societal acceptance remains a challenge. Therefore, we must consider ways to promote prosocial behavior in micromobility interactions. We investigate how post-ride feedb...

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 societal acceptance remains a challenge. Therefore, we must consider ways to promote prosocial behavior in micromobility interactions. We investigate how post-ride feedb...

Cooperative automated vehicles (AVs) bring the potential for better safety, efficiency, and energy-savings on the individual and system level. Yet, these benefits can only be achieved if people cooperate. In this study, we explored the effects of cooperative and reciprocal AVs on people's well-being, trust, and cooperation. We conducted a mixed-des...

Rapid advances in shared mobility and vehicle automation have led to a monumental shift of interest toward developing automated systems. However, automation development has wider implications for how end users would trust these novel automation systems. This research explored how the experience with prior automation systems and exposure to vehicles...

Recent advances in Automated vehicle (AV) technology and micromobility devices promise a transformational change in the future of mobility usage. These advances also pose challenges concerning human-AV interactions. To ensure the smooth adoption of these new mobilities, it is essential to assess how past experiences and perceptions of social intera...

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 c...

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...

The ever-increasing adoption of shared transportation modalities across the United States has the potential to fundamentally change the preferences and usage of different mobilities. It also raises several challenges with respect to the design and development of automated mobilities that can enable a large population to take advantage of this emerg...

The ever-increasing adoption of shared transportation modalities across the United States has the potential to fundamentally change the preferences and usage of different mobilities. It also raises several challenges with respect to the design and development of automated mobilities that can enable a large population to take advantage of this emerg...

In automated driving, it is important to maintain drivers’ situational awareness (SA) in order to help them avoid unnecessary interventions and negotiate challenging scenarios where human takeovers are needed. Our study developed computational models to predict a driver’s SA of a target object. Using the SEEV (Salience, Effort, Expectancy, and Valu...

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 autonomous vehicles (AVs), SAVs will likely be deployed before privately-owned AVs. Moreover, existing shared mobility services are transitioning their vehicle fleets toward tho...

The objective of this study is to assess drivers’ ability to detect objects and the trajectory of these objects in scenarios with different environmental complexity levels. This is examined in the context of situation awareness (SA), defined as theperception, comprehension and projection of the environmental properties andpositions. The Situation A...

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 t...

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 autonomous vehicles (AVs), SAVs will likely be deployed before privately-owned AVs. Moreover, existing shared mobility services are transitioning their vehicle fleets toward tho...

Computational models embedded in advanced driver assistance systems (ADAS) require insights on drivers’ perception and understanding of their environment. This is particularly important as vehicles become increasingly automated and the partnership between the controllers (driver or vehicle) needs to be attentive to each other’s future intentions. T...

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 consisten...

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, displayi...

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 consisten...

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 drivi...

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 dy...

In the present paper, we present a user study with an advanced-driver assistance system (ADAS) using augmented reality (AR) cues to highlight pedestrians and vehicles when approaching intersections of varying complexity. Our major goal is to understand the relationship between the presence and absence of AR, driver-initiated takeover rates and glan...

Aomation has become prevalent in the everyday lives of humans. However, despite significant technological advancements, human supervision and intervention are still necessary in almost all sectors of automation, ranging from manufacturing and transportation to disaster management and health care [1]. Therefore, it is expected that the future will b...

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 dec...

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-ma...

Intelligent machines, and more broadly, intelligent systems, are becoming increasingly common in the everyday lives of humans. Nonetheless, despite significant advancements in automation, human supervision and intervention are still essential in almost all sectors, ranging from manufacturing and transportation to disaster-management and healthcare....

Intelligent and autonomous technology is performing tasks from driving to healthcare, in which calibration of human trust in automation is critical. In this paper, we use a model-based clustering algorithm to identify and model dominant dynamics of human trust in automation among a general population. In doing so, we seek to balance the tradeoffs b...

To attain improved human-machine collaboration, it is necessary for autonomous systems to infer human trust and workload and respond accordingly. In turn, autonomous systems require models that capture both human trust and workload dynamics. In a companion paper, we developed a trust-workload partially observable Markov decision process (POMDP) mod...

In this paper, we establish a partially observable Markov decision process (POMDP) model framework that captures dynamic changes in human trust and workload for contexts that involve interactions between humans and intelligent decision-aid systems. We use a reconnaissance mission study to elicit a dynamic change in human trust and workload with res...

We developed an experiment to elicit human trust dynamics in human–machine interaction contexts and established a quantitative model of human trust behavior with respect to these contexts. The proposed model describes human trust level as a function of experience, cumulative trust, and expectation bias. We estimated the model parameters using human...

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-...

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-...

In an increasingly automated world, trust between humans and autonomous systems is critical for successful integration of these systems into our daily lives. In particular, for autonomous systems to work cooperatively with humans, they must be able to sense and respond to the trust of the human. This inherently requires a control-oriented model of...

Human trust in automation plays an important role in successful interactions between humans and machines. To design intelligent machines that can respond to changes in human trust, real-time sensing of trust level is needed. In this paper, we describe an empirical trust sensor model that maps psychophysiological measurements to human trust level. T...

The Active Magnetic Bearings (AMBs) need a closed loop feedback controller for its stable operation. In the present work, a method has been proposed to design the preliminary controller for an unknown AMB system with knowledge of the maximum value of current that can be supplied to electromagnet and sampling rate of data acquisition system. This in...

A procedure to design eight-pole electromagnetic bearing has been described. Necessary force and constraints equations have been detailed. Geometric limitations have been considered. Constraints are applied on losses occurring in the electromagnet. The magnetic force has been maximized using geometric programming method, which is easier to understa...

The Active Magnetic Bearings (AMBs) need a closed loop feedback controller for its stable operation. In the present work a method has been proposed to design the preliminary controller for an unknown AMB system with knowledge of the maximum value of current that can be supplied to electromagnet and sampling rate of data acquisition system. This ini...