17 research outputs found
How do Environmental Factors Affect Drivers’ Gaze and Head Movements?
Studies have shown that environmental factors affect driving behaviors. For instance, weather conditions and the presence of a passenger have been shown to significantly affect the speed of the driver. As one of the important measures of driving behavior is the gaze and head movements of the driver, such metrics can be potentially used towards understanding the effects of environmental factors on the driver’s behavior in real-time. In this study, using a naturalistic study platform, videos have been collected from six participants for more than four weeks of a fully naturalistic driving scenario. The videos of both the participants’ faces and roads have been cleaned and manually categorized depending on weather, road type, and passenger conditions. Facial videos have been analyzed using OpenFace to retrieve the gaze direction and head movements of the driver. Results, overall, suggest that the gaze direction and head movements of the driver are affected by a combination of environmental factors and individual differences. Specifically, results depict the distracting effect of the passenger on some individuals. In addition, it shows that highways and city streets are the cause for maximum distraction on the driver’s gaze
Occupant Privacy Perception, Awareness, and Preferences in Smart Office Environments
Building management systems tout numerous benefits, such as energy efficiency
and occupant comfort but rely on vast amounts of data from various sensors.
Advancements in machine learning algorithms make it possible to extract
personal information about occupants and their activities beyond the intended
design of a non-intrusive sensor. However, occupants are not informed of data
collection and possess different privacy preferences and thresholds for privacy
loss. While privacy perceptions and preferences are most understood in smart
homes, limited studies have evaluated these factors in smart office buildings,
where there are more users and different privacy risks. To better understand
occupants' perceptions and privacy preferences, we conducted twenty-four
semi-structured interviews between April 2022 and May 2022 on occupants of a
smart office building. We found that data modality features and personal
features contribute to people's privacy preferences. The features of the
collected modality define data modality features -- spatial, security, and
temporal context. In contrast, personal features consist of one's awareness of
data modality features and data inferences, definitions of privacy and
security, and the available rewards and utility. Our proposed model of people's
privacy preferences in smart office buildings helps design more effective
measures to improve people's privacy
A Multimodal Approach for Monitoring Driving Behavior and Emotions
Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, a smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s affect is highly influenced by the type of road and the weather conditions, which have the potential to change driving behaviors. For instance, when the research defines emotional metrics as valence and engagement, results reveal there exist significant differences between human emotion in different weather conditions and road types. Participants’ engagement was higher in rainy and clear weather compared to cloudy weather. More-over, engagement was higher on city streets and highways compared to one-lane roads and two-lane highways
Building Performance Simulations Can Inform IoT Privacy Leaks in Buildings
As IoT devices become cheaper, smaller, and more ubiquitously deployed, they
can reveal more information than their intended design and threaten user
privacy. Indoor Environmental Quality (IEQ) sensors previously installed for
energy savings and indoor health monitoring have emerged as an avenue to infer
sensitive occupant information. For example, light sensors are a known conduit
for inspecting room occupancy status with motion-sensitive lights. Light
signals can also infer sensitive data such as occupant identity and digital
screen information. To limit sensor overreach, we explore the selection of
sensor placements as a methodology. Specifically, in this proof-of-concept
exploration, we demonstrate the potential of physics-based simulation models to
quantify the minimal number of positions necessary to capture sensitive
inferences. We show how a single well-placed sensor can be sufficient in
specific building contexts to holistically capture its environmental states and
how additional well-placed sensors can contribute to more granular inferences.
We contribute a device-agnostic and building-adaptive workflow to respectfully
capture inferable occupant activity and elaborate on the implications of
incorporating building simulations into sensing schemes in the real world
A multi-agent systems for design simulation framework:experiments with virtual-physical-social feedback for architecture
This paper presents research on the development of multiagent systems (MAS) for integrated and performance driven architectural design. It presents the development of a simulation framework that bridges architecture and engineering, through a series of multi-agent based experiments. The research is motivated to combine multiple design agencies into a system for managing and optimizing architectural form, across multiple objectives and contexts. The research anticipates the incorporation of feedback from real world human behavior and user preferences with physics based structural form finding and environmental analysis data. The framework is a multi-agent system that provides design teams with informed design solutions, which simultaneously optimize and satisfy competing design objectives. The initial results for building structures are measured in terms of the level of lighting improvements and qualitatively in geometric terms. Critical to the research is the elaboration of the system and the feedback loops that are possible when using the multi-agent systems approach
Rethinking infrastructure design: Evaluating pedestrians and VRUs' psychophysiological and behavioral responses to different roadway designs
The integration of human-centric approaches has gained more attention
recently due to more automated systems being introduced into our built
environments (buildings, roads, vehicles, etc.), which requires a correct
understanding of how humans perceive such systems and respond to them. This
paper introduces an Immersive Virtual Environment-based method to evaluate the
infrastructure design with psycho-physiological and behavioral responses from
the vulnerable road users, especially for pedestrians. A case study of
pedestrian mid-block crossings with three crossing infrastructure designs
(painted crosswalk, crosswalk with flashing beacons, and a smartphone app for
connected vehicles) are tested. Results from 51 participants indicate there are
differences between the subjective and objective measurement. A higher
subjective safety rating is reported for the flashing beacon design, while the
psychophysiological and behavioral data indicate that the flashing beacon and
smartphone app are similar in terms of crossing behaviors, eye tracking
measurements, and heart rate. In addition, the smartphone app scenario appears
to have a lower stress level as indicated by eye tracking data, although many
participants don't have prior experience with it. Suggestions are made for the
implementation of new technologies, which can increase public acceptance of new
technologies and pedestrian safety in the future
Exploring Smart Commercial Building Occupants' Perceptions and Notification Preferences of Internet of Things Data Collection in the United States
Data collection through the Internet of Things (IoT) devices, or smart
devices, in commercial buildings enables possibilities for increased
convenience and energy efficiency. However, such benefits face a large
perceptual challenge when being implemented in practice, due to the different
ways occupants working in the buildings understand and trust in the data
collection. The semi-public, pervasive, and multi-modal nature of data
collection in smart buildings points to the need to study occupants'
understanding of data collection and notification preferences. We conduct an
online study with 492 participants in the US who report working in smart
commercial buildings regarding: 1) awareness and perception of data collection
in smart commercial buildings, 2) privacy notification preferences, and 3)
potential factors for privacy notification preferences. We find that around
half of the participants are not fully aware of the data collection and use
practices of IoT even though they notice the presence of IoT devices and
sensors. We also discover many misunderstandings around different data
practices. The majority of participants want to be notified of data practices
in smart buildings, and they prefer push notifications to passive ones such as
websites or physical signs. Surprisingly, mobile app notification, despite
being a popular channel for smart homes, is the least preferred method for
smart commercial buildings.Comment: EuroS&P 2023 camera read
The Impact of Surrounding Road Objects and Conditions on Drivers Abrupt Heart Rate Changes
Recent studies have pointed out the importance of mitigating drivers stress
and negative emotions. These studies show that certain road objects such as big
vehicles might be associated with higher stress levels based on drivers
subjective stress measures. Additionally, research shows strong correlations
between drivers stress levels and increased heart rate (HR). In this paper,
based on a naturalistic multimodal driving dataset, we analyze the visual
scenes of driving in the vicinity of abrupt increases in drivers HR for the
presence of certain stress-inducing road objects. We show that the probability
of the presence of such objects increases when becoming closer to the abrupt
increase in drivers HR. Additionally, we show that drivers facial engagement
changes significantly in the vicinity of abrupt increases in HR. Our results
lay the ground for a human-centered driving experience by detecting and
mitigating drivers stress levels in the wild.Comment: Accepted to 66th Human Factors and Ergonomics Society International
Annual Meeting 202
Building performance simulations can inform IoT privacy leaks in buildings
Abstract As IoT devices become cheaper, smaller, and more ubiquitously deployed, they can reveal more information than their intended design and threaten user privacy. Indoor Environmental Quality (IEQ) sensors previously installed for energy savings and indoor health monitoring have emerged as an avenue to infer sensitive occupant information. For example, light sensors are a known conduit for inspecting room occupancy status with motion-sensitive lights. Light signals can also infer sensitive data such as occupant identity and digital screen information. To limit sensor overreach, we explore the selection of sensor placements as a methodology. Specifically, in this proof-of-concept exploration, we demonstrate the potential of physics-based simulation models to quantify the minimal number of positions necessary to capture sensitive inferences. We show how a single well-placed sensor can be sufficient in specific building contexts to holistically capture its environmental states and how additional well-placed sensors can contribute to more granular inferences. We contribute a device-agnostic and building-adaptive workflow to respectfully capture inferable occupant activity and elaborate on the implications of incorporating building simulations into sensing schemes in the real world