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

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Analysis and Observations from the First Amazon Picking Challenge

This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned based on survey results and the authors' personal experiences during the challenge