Jobs at Risk!? Effects of Automation of Jobs on Occupational Mobility

The paper investigates the relationship between the risk of automation of jobs and individual-level occupational mobility using a representative German household survey. The results suggest that expected occupational changes such as losing a job and demotion at the current place of employment, among others, are likely to be driven by the high occupation-specific risk of automation. However, switches to self-employment are more likely to occur from occupations with low risk of automation

Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning

Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driving situation. In this work, we present a novel approach for the dynamic risk assessment of driving situations based on images of a front stereo camera using deep learning. To this end, we trained a deep neural network with recorded monocular images, disparity maps and a risk metric for diverse traffic scenes. Our approach can be used to create the aforementioned situation awareness of vehicles of higher automation levels and can serve as a heterogeneous channel to systems based on radar or lidar sensors that are used traditionally for the calculation of risk metrics

The problem of automation: Inappropriate feedback and interaction, not overautomation

As automation increasingly takes its place in industry, especially high-risk industry, it is often blamed for causing harm and increasing the chance of human error when failures occur. It is proposed that the problem is not the presence of automation, but rather its inappropriate design. The problem is that the operations are performed appropriately under normal conditions, but there is inadequate feedback and interaction with the humans who must control the overall conduct of the task. When the situations exceed the capabilities of the automatic equipment, then the inadequate feedback leads to difficulties for the human controllers. The problem is that the automation is at an intermediate level of intelligence, powerful enough to take over control that which used to be done by people, but not powerful enough to handle all abnormalities. Moreover, its level of intelligence is insufficient to provide the continual, appropriate feedback that occurs naturally among human operators. To solve this problem, the automation should either be made less intelligent or more so, but the current level is quite inappropriate. The overall message is that it is possible to reduce error through appropriate design considerations

FPR -- Fast Path Risk Algorithm to Evaluate Collision Probability

As mobile robots and autonomous vehicles become increasingly prevalent in human-centred environments, there is a need to control the risk of collision. Perceptual modules, for example machine vision, provide uncertain estimates of object location. In that context, the frequently made assumption of an exactly known free-space is invalid. Clearly, no paths can be guaranteed to be collision free. Instead, it is necessary to compute the probabilistic risk of collision on any proposed path. The FPR algorithm, proposed here, efficiently calculates an upper bound on the risk of collision for a robot moving on the plane. That computation orders candidate trajectories according to (the bound on) their degree of risk. Then paths within a user-defined threshold of primary risk could be selected according to secondary criteria such as comfort and efficiency. The key contribution of this paper is the FPR algorithm and its `convolution trick' to factor the integrals used to bound the risk of collision. As a consequence of the convolution trick, given $K$ obstacles and $N$ candidate paths, the computational load is reduced from the naive $O(NK)$, to the qualitatively faster $O(N+K)$.Comment: To appear in IEEE Robotics and Automation Letters (RA-L

A focus on cross-purpose tools, automated recognition of study design in multiple disciplines, and evaluation of automation tools: a summary of significant discussions at the fourth meeting of the International Collaboration for Automation of Systematic Reviews (ICASR)

The fourth meeting of the International Collaboration for Automation of Systematic Reviews (ICASR) was held 5–6 November 2019 in The Hague, the Netherlands. ICASR is an interdisciplinary group whose goal is to maximize the use of technology for conducting rapid, accurate, and efficient systematic reviews of scientific evidence. The group seeks to facilitate the development and acceptance of automated techniques for systematic reviews. In 2018, the major themes discussed were the transferability of automation tools (i.e., tools developed for other purposes that might be used by systematic reviewers), the automated recognition of study design in multiple disciplines and applications, and approaches for the evaluation of automation tools

Automotive automation: Investigating the impact on drivers' mental workload

Recent advances in technology have meant that an increasing number of vehicle driving tasks are becoming automated. Such automation poses new problems for the ergonomist. Of particular concern in this paper are the twofold effects of automation on mental workload - novel technologies could increase attentional demand and workload, alternatively one could argue that fewer driving tasks will lead to the problem of reduced attentional demand and driver underload. A brief review of previous research is presented, followed by an overview of current research taking place in the Southampton Driving Simulator. Early results suggest that automation does reduce workload, and that underload is indeed a problem, with a significant proportion of drivers unable to effectively reclaim control of the vehicle in an automation failure scenario. Ultimately, this research and a subsequent program of studies will be interpreted within the framework of a recently proposed theory of action, with a view to maximizing both theoretical and applied benefits of this domain