Viewpoint Of Cognitive Science Research Articles

Autonomous driving: cognitive construction and situation understanding

Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of `perception, planning, decision-making, and control. It is necessary to explore a basic computing framework that conforms to human drivers attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence.In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios.The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment.

Epic Theatre Reexamined from the Viewpoint of Cognitive Science

Epic Theatre Reexamined from the Viewpoint of Cognitive Science

Hierarchical Mesh Segmentation Based on Global Sharp Vertices

In this paper, we propose a hierarchical method for segmenting a given 3D mesh, which hierarchically clusters sharp vertices of the mesh using the metric of geodesic distance among them. Sharp vertices are extracted from the mesh by analyzing convexity that reflects global geometry. As well as speeding up the computing time, the sharp vertices of this kind avoid the problem of local optima that may occur when feature points are extracted by analyzing the convexity that reflects local geometry. For obtaining more effective results, the sharp vertices are categorized according to the priority from the viewpoint of cognitive science, and the reasonable number of clusters is automatically determined by analyzing the geometric features of the mesh.

첨예정점의 측지거리 평균군집화를 이용한 메쉬 분할

In this paper, we adapt the -means clustering technique to segmenting a given 3D mesh. In order to avoid the locally minimal convergence and speed up the computing time, first we extract sharp vertices from the mesh by analysing its curvature and convexity that respectively reflect the local and global geometric characteristics from the viewpoint of cognitive science. Next the sharp vertices are partitioned into clusters by iterated converging with the -means clustering method based on the geodesic distance instead of the Euclidean distance between each pair of the sharp vertices. For obtaining the effective result of -means clustering method, it is crucial to assign an initial value to appropriately. Hence, we automatically compute a reasonable number of clusters as an initial value of . Finally the mesh segmentation is completed by merging other vertices except the sharp vertices into the nearest cluster by geodesic distance.

Robovie: an interactive humanoid robot

The authors have developed a robot called “Robovie” that has unique mechanisms designed for communication with humans. Robovie can generate human‐like behaviors by using human‐like actuators and vision and audio sensors. Software is a key element in the systems development. Two important ideas in human‐robot communication through research from the viewpoint of cognitive science have been obtained – one is importance of physical expressions using the body and the other is the effectiveness of the robot’s autonomy in the robot’s utterance recognition by humans. Based on these psychological experiments, a new architecture that generates episode chains in interactions with humans is developed. The basic structure of the architecture is a network of situated modules. Each module consists of elemental behaviors to entrain humans and a behavior for communicating with humans.

Association‐based parser for speech understanding systems—framework design on cognitive exploration

AbstractIt is well known that the use of the high‐level information data such as language information is indispensable in the speech understanding system. Because the human speech understanding process is executed unconsciously, sufficient knowledge is not yet available concerning what kind of high‐level information data are utilized and in what ways. We made two kinds of studies from the viewpoint of cognitive science to acquire knowledge about the use of high‐level information. Based on the result of study, an association based parser ASP was realized for the speech understanding system. ASP receives a large number of phrase candidate sequences as the input, and identifies the correct phrase sequence by examining successively the available syntactic, semantic and associative information. Especially, the topic is extracted first from the input candidates as a whole, and an association mechanism is employed where the vocabulary associated from the topic is processed with priority, being considered as an active word. The study in this paper is described from the viewpoint of cognitive science, and the framework of the language processing system obtained from the study is presented.