8,394 research outputs found
Ethical and Social Aspects of Self-Driving Cars
As an envisaged future of transportation, self-driving cars are being
discussed from various perspectives, including social, economical, engineering,
computer science, design, and ethics. On the one hand, self-driving cars
present new engineering problems that are being gradually successfully solved.
On the other hand, social and ethical problems are typically being presented in
the form of an idealized unsolvable decision-making problem, the so-called
trolley problem, which is grossly misleading. We argue that an applied
engineering ethical approach for the development of new technology is what is
needed; the approach should be applied, meaning that it should focus on the
analysis of complex real-world engineering problems. Software plays a crucial
role for the control of self-driving cars; therefore, software engineering
solutions should seriously handle ethical and social considerations. In this
paper we take a closer look at the regulative instruments, standards, design,
and implementations of components, systems, and services and we present
practical social and ethical challenges that have to be met, as well as novel
expectations for software engineering.Comment: 11 pages, 3 figures, 2 table
Health and safety in the United Kingdom higher education libraries: a review of the literature
The focus of this article is to review the literature relating to health and safety in UK Higher Education libraries. This will include an overview of the literature on accident theories and also the human element. Various key findings emerge from this analysis. Personal safety is achieved through self-responsibility, following guidelines and having a working knowledge of reporting procedures. A safety culture in the work environment is developed through a proactive approach on the part of management, the provision of information, training, and carrying out safety inspections. These inspections are aimed at preventing the environment from creating a situation where an accident could occur. There can never be a work environment in which no accidents will occur and best practice can only minimize the risk of accidents
Detecting Distracted Driving with Deep Learning
© Springer International Publishing AG 2017Driver distraction is the leading factor in most car crashes and near-crashes. This paper discusses the types, causes and impacts of distracted driving. A deep learning approach is then presented for the detection of such driving behaviors using images of the driver, where an enhancement has been made to a standard convolutional neural network (CNN). Experimental results on Kaggle challenge dataset have confirmed the capability of a convolutional neural network (CNN) in this complicated computer vision task and illustrated the contribution of the CNN enhancement to a better pattern recognition accuracy.Peer reviewe
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots
Safety is paramount for mobile robotic platforms such as self-driving cars
and unmanned aerial vehicles. This work is devoted to a task that is
indispensable for safety yet was largely overlooked in the past -- detecting
obstacles that are of very thin structures, such as wires, cables and tree
branches. This is a challenging problem, as thin objects can be problematic for
active sensors such as lidar and sonar and even for stereo cameras. In this
work, we propose to use video sequences for thin obstacle detection. We
represent obstacles with edges in the video frames, and reconstruct them in 3D
using efficient edge-based visual odometry techniques. We provide both a
monocular camera solution and a stereo camera solution. The former incorporates
Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter
enjoys a novel, purely vision-based solution. Experiments demonstrated that the
proposed methods are fast and able to detect thin obstacles robustly and
accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio
Parallelized Interactive Machine Learning on Autonomous Vehicles
Deep reinforcement learning (deep RL) has achieved superior performance in
complex sequential tasks by learning directly from image input. A deep neural
network is used as a function approximator and requires no specific state
information. However, one drawback of using only images as input is that this
approach requires a prohibitively large amount of training time and data for
the model to learn the state feature representation and approach reasonable
performance. This is not feasible in real-world applications, especially when
the data are expansive and training phase could introduce disasters that affect
human safety. In this work, we use a human demonstration approach to speed up
training for learning features and use the resulting pre-trained model to
replace the neural network in the deep RL Deep Q-Network (DQN), followed by
human interaction to further refine the model. We empirically evaluate our
approach by using only a human demonstration model and modified DQN with human
demonstration model included in the Microsoft AirSim car simulator. Our results
show that (1) pre-training with human demonstration in a supervised learning
approach is better and much faster at discovering features than DQN alone, (2)
initializing the DQN with a pre-trained model provides a significant
improvement in training time and performance even with limited human
demonstration, and (3) providing the ability for humans to supply suggestions
during DQN training can speed up the network's convergence on an optimal
policy, as well as allow it to learn more complex policies that are harder to
discover by random exploration.Comment: 6 pages, NAECON 2018 - IEEE National Aerospace and Electronics
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