23,199 research outputs found
High quality indoor environments for sustainable office buildings
The quality of office indoor environments is considered to consist of those factors that impact
occupants according to their health and well-being and (by consequence) their productivity.
Indoor Environment Quality (IEQ) can be characterized by four indicators:
• Indoor air quality indicators
• Thermal comfort indicators
• Lighting indicators
• Noise indicators.
Within each indicator, there are specific metrics that can be utilized in determining an
acceptable quality of an indoor environment based on existing knowledge and best practice.
Examples of these metrics are: indoor air levels of pollutants or odorants; operative
temperature and its control; radiant asymmetry; task lighting; glare; ambient noise. The way
in which these metrics impact occupants is not fully understood, especially when multiple
metrics may interact in their impacts. While the potential cost of lost productivity from poor
IEQ has been estimated to exceed building operation costs, the level of impact and the
relative significance of the above four indicators are largely unknown. However, they are key
factors in the sustainable operation or refurbishment of office buildings.
This paper presents a methodology for assessing indoor environment quality (IEQ) in office
buildings, and indicators with related metrics for high performance and occupant comfort.
These are intended for integration into the specification of sustainable office buildings as
key factors to ensure a high degree of occupant habitability, without this being impaired by
other sustainability factors.
The assessment methodology was applied in a case study on IEQ in Australia’s first ‘six star’
sustainable office building, Council House 2 (CH2), located in the centre of Melbourne. The
CH2 building was designed and built with specific focus on sustainability and the provision of
a high quality indoor environment for occupants. Actual IEQ performance was assessed in
this study by field assessment after construction and occupancy. For comparison, the
methodology was applied to a 30 year old conventional building adjacent to CH2 which
housed the same or similar occupants and activities. The impact of IEQ on occupant
productivity will be reported in a separate future pape
Screening of energy efficient technologies for industrial buildings' retrofit
This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit
Human-activity-centered measurement system:challenges from laboratory to the real environment in assistive gait wearable robotics
Assistive gait wearable robots (AGWR) have shown a great advancement in developing intelligent devices to assist human in their activities of daily living (ADLs). The rapid technological advancement in sensory technology, actuators, materials and computational intelligence has sped up this development process towards more practical and smart AGWR. However, most assistive gait wearable robots are still confined to be controlled, assessed indoor and within laboratory environments, limiting any potential to provide a real assistance and rehabilitation required to humans in the real environments. The gait assessment parameters play an important role not only in evaluating the patient progress and assistive device performance but also in controlling smart self-adaptable AGWR in real-time. The self-adaptable wearable robots must interactively conform to the changing environments and between users to provide optimal functionality and comfort. This paper discusses the performance parameters, such as comfortability, safety, adaptability, and energy consumption, which are required for the development of an intelligent AGWR for outdoor environments. The challenges to measuring the parameters using current systems for data collection and analysis using vision capture and wearable sensors are presented and discussed
Improving Automated Driving through Planning with Human Internal States
This work examines the hypothesis that partially observable Markov decision
process (POMDP) planning with human driver internal states can significantly
improve both safety and efficiency in autonomous freeway driving. We evaluate
this hypothesis in a simulated scenario where an autonomous car must safely
perform three lane changes in rapid succession. Approximate POMDP solutions are
obtained through the partially observable Monte Carlo planning with observation
widening (POMCPOW) algorithm. This approach outperforms over-confident and
conservative MDP baselines and matches or outperforms QMDP. Relative to the MDP
baselines, POMCPOW typically cuts the rate of unsafe situations in half or
increases the success rate by 50%.Comment: Preprint before submission to IEEE Transactions on Intelligent
Transportation Systems. arXiv admin note: text overlap with arXiv:1702.0085
Automatic and efficient driving strategies while approaching a traffic light
Vehicle-infrastructure communication opens up new ways to improve traffic
flow efficiency at signalized intersections. In this study, we assume that
equipped vehicles can obtain information about switching times of relevant
traffic lights in advance. This information is used to improve traffic flow by
the strategies 'early braking', 'anticipative start', and 'flying start'. The
strategies can be implemented in driver-information mode, or in automatic mode
by an Adaptive Cruise Controller (ACC). Quality criteria include cycle-averaged
capacity, driving comfort, fuel consumption, travel time, and the number of
stops. By means of simulation, we investigate the isolated strategies and the
complex interactions between the strategies and between equipped and
non-equipped vehicles. As universal approach to assess equipment level effects
we propose relative performance indexes and found, at a maximum speed of 50
km/h, improvements of about 15% for the number of stops and about 4% for the
other criteria. All figures double when increasing the maximum speed to 70
km/h.Comment: Submitted to ITSC - 17th International IEEE Conference on Intelligent
Transportation System
Neural-Network Based Adaptive Proxemics-Costmap for Human-Aware Autonomous Robot Navigation
In the revolution of Industry 4.0, autonomous robot navigation plays a vital role in ensuring intelligent cooperation with human workers to increase manufacturing efficiency. Human prefers to maintain a proxemic distance with other subjects for safety and comfort purposes, where the human personal-space can be represented by a costmap. Current proxemic costmaps perform well in defining the proxemic boundary to maintain the human-robot proxemic distance. However, these approaches generate static costmaps that are not adaptive towards different human states (linear position, angular position and velocity). This problem impacts the robot navigation efficiency, reduces human safety and comfort as the autonomous robot failed to prioritize avoiding certain humans over the other. To overcome this drawback, this paper proposed a neural-network based adaptive proxemic-costmap, named as NNPC, that can generate different sized personal-spaces at different human state encounters. The proposed proxemic-costmap was developed by learning a neural-network model using real human state data. A total of three human scenarios were used for data collection. The data were collected by tracking the humans in video recordings. After the model was trained, the proposed NNPC costmap was evaluated against two other state-of-art proxemic costmaps in five simulated human scenarios with various human states. Results show that NNPC outperformed the compared costmaps by ensuring human-aware robot manoeuvres that have higher robot efficiency and increased human safety and comfort.
 
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model
In the coming years and decades, autonomous vehicles (AVs) will become
increasingly prevalent, offering new opportunities for safer and more
convenient travel and potentially smarter traffic control methods exploiting
automation and connectivity. Car following is a prime function in autonomous
driving. Car following based on reinforcement learning has received attention
in recent years with the goal of learning and achieving performance levels
comparable to humans. However, most existing RL methods model car following as
a unilateral problem, sensing only the vehicle ahead. Recent literature,
however, Wang and Horn [16] has shown that bilateral car following that
considers the vehicle ahead and the vehicle behind exhibits better system
stability. In this paper we hypothesize that this bilateral car following can
be learned using RL, while learning other goals such as efficiency
maximisation, jerk minimization, and safety rewards leading to a learned model
that outperforms human driving.
We propose and introduce a Deep Reinforcement Learning (DRL) framework for
car following control by integrating bilateral information into both state and
reward function based on the bilateral control model (BCM) for car following
control. Furthermore, we use a decentralized multi-agent reinforcement learning
framework to generate the corresponding control action for each agent. Our
simulation results demonstrate that our learned policy is better than the human
driving policy in terms of (a) inter-vehicle headways, (b) average speed, (c)
jerk, (d) Time to Collision (TTC) and (e) string stability
What Truly Matters in Trajectory Prediction for Autonomous Driving?
In the autonomous driving system, trajectory prediction plays a vital role in
ensuring safety and facilitating smooth navigation. However, we observe a
substantial discrepancy between the accuracy of predictors on fixed datasets
and their driving performance when used in downstream tasks. This discrepancy
arises from two overlooked factors in the current evaluation protocols of
trajectory prediction: 1) the dynamics gap between the dataset and real driving
scenario; and 2) the computational efficiency of predictors. In real-world
scenarios, prediction algorithms influence the behavior of autonomous vehicles,
which, in turn, alter the behaviors of other agents on the road. This
interaction results in predictor-specific dynamics that directly impact
prediction results. As other agents' responses are predetermined on datasets, a
significant dynamics gap arises between evaluations conducted on fixed datasets
and actual driving scenarios. Furthermore, focusing solely on accuracy fails to
address the demand for computational efficiency, which is critical for the
real-time response required by the autonomous driving system. Therefore, in
this paper, we demonstrate that an interactive, task-driven evaluation approach
for trajectory prediction is crucial to reflect its efficacy for autonomous
driving
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